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Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer
hurshd0/must-read-papers-for-ml
Folders and files, repository files navigation, must read papers for data science, ml, and dl, curated collection of data science, machine learning and deep learning papers, reviews and articles that are on must read list..
NOTE: ๐ง in process of updating, let me know what additional papers, articles, blogs to add I will add them here.
๐ โญ this repo
Contributing
- ๐ ๐ Please feel free to Submit Pull Request , if links are broken, or I am missing any important papers, blogs or articles.
๐ READ THIS ๐
- ๐ Reading paper with heavy math is hard, it takes time and effort to understand, most of it is dedication and motivation to not quit, don't be discouraged, read once, read twice, read thrice,... until it clicks and blows you away.
๐ฅ - Read it first
๐ฅ - Read it second
๐ฅ - Read it third
Data Science
๐ pre-processing & eda.
๐ฅ ๐ Data preprocessing - Tidy data - by Hadley Wickham
๐ General DS
๐ฅ ๐ Statistical Modeling: The Two Cultures - by Leo Breiman
๐ฅ ๐ A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning
- ๐น KDD 2019 Cynthia Rudin's Keynote
๐ฅ ๐ Frequentism and Bayesianism: A Python-driven Primer by Jake VanderPlas
Machine Learning
๐ฏ general ml.
๐ฅ ๐ Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning - by Sebastian Raschka
๐ฅ ๐ A Brief Introduction into Machine Learning - by Gunnar Ratsch
๐ฅ ๐ An Introduction to the Conjugate Gradient Method Without the Agonizing Pain - by Jonathan Richard Shewchuk
๐ฅ ๐ On Model Stability as a Function of Random Seed
๐ Outlier/Anomaly detection
๐ฅ ๐ฐ Outlier Detection : A Survey
๐ฅ ๐ XGBoost: A Scalable Tree Boosting System
๐ฅ ๐ LightGBM: A Highly Efficient Gradient BoostingDecision Tree
๐ฅ ๐ AdaBoost and the Super Bowl of Classifiers - A Tutorial Introduction to Adaptive Boosting
๐ฅ ๐ Greedy Function Approximation: A Gradient Boosting Machine
๐ Unraveling Blackbox ML
๐ฅ ๐ Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation
๐ฅ ๐ Data Shapley: Equitable Valuation of Data for Machine Learning
โ๏ธ Dimensionality Reduction
๐ฅ ๐ A Tutorial on Principal Component Analysis
๐ฅ ๐ How to Use t-SNE Effectively
๐ฅ ๐ Visualizing Data using t-SNE
๐ Optimization
๐ฅ ๐ A Tutorial on Bayesian Optimization
๐ฅ ๐ Taking the Human Out of the Loop: A review of Bayesian Optimization
Famous Blogs
Sebastian Raschka Chip Huyen
๐ฑ ๐ฎ Recommenders
๐ฅ ๐ A Survey of Collaborative Filtering Techniques
๐ฅ ๐ Collaborative Filtering Recommender Systems
๐ฅ ๐ Deep Learning Based Recommender System: A Survey and New Perspectives
๐ฅ ๐ ๐ค โญ Explainable Recommendation: A Survey and New Perspectives โญ
Case Studies
๐ฅ ๐ The Netflix Recommender System: Algorithms, Business Value,and Innovation
- Netflix Recommendations: Beyond the 5 stars Part 1
- Netflix Recommendations: Beyond the 5 stars Part 2
๐ฅ ๐ Two Decades of Recommender Systems at Amazon.com
๐ฅ ๐ How Does Spotify Know You So Well?
๐ More In-Depth study, ๐ Recommender Systems Handbook
Famous Deep Learning Blogs ๐ค
๐ Stanford UFLDL Deep Learning Tutorial
๐ Distill.pub
๐ Colah's Blog
๐ Andrej Karpathy
๐ Zack Lipton
๐ Sebastian Ruder
๐ Jay Alammar
๐ Neural Networks and Deep Learning Neural Networks
โญ ๐ฅ ๐ฐ The Matrix Calculus You Need For Deep Learning - Terence Parr and Jeremy Howard โญ
๐ฅ ๐ฐ Deep learning -Yann LeCun, Yoshua Bengio & Geoffrey Hinton
๐ฅ ๐ Generalization in Deep Learning
๐ฅ ๐ Topology of Learning in Artificial Neural Networks
๐ฅ ๐ Dropout: A Simple Way to Prevent Neural Networks from Overfitting
๐ฅ ๐ Polynomial Regression As an Alternative to Neural Nets
๐ฅ ๐ The Neural Network Zoo
๐ฅ ๐ Image Completion with Deep Learning in TensorFlow
๐ฅ ๐ Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
๐ฅ ๐ A systematic study of the class imbalance problem in convolutional neural networks
๐ฅ ๐ All Neural Networks are Created Equal
๐ฅ ๐ Adam: A Method for Stochastic Optimization
๐ฅ ๐ AutoML: A Survey of the State-of-the-Art
๐ฅ ๐ Visualizing and Understanding Convolutional Networks -by Andrej Karpathy Justin Johnson Li Fei-Fei
๐ฅ ๐ Deep Residual Learning for Image Recognition
๐ฅ ๐ AlexNet-ImageNet Classification with Deep Convolutional Neural Networks
๐ฅ ๐ VGG Net-VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
๐ฅ ๐ A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
๐ฅ ๐ Large-scale Video Classification with Convolutional Neural Networks
๐ฅ ๐ Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
โซ CapsNet ๐ฑ
๐ฅ ๐ Dynamic Routing Between Capsules
Blog explaning, "What are CapsNet, or Capsule Networks?"
Capsule Networks Tutorial by Aureline Geron
๐๏ธ ๐ฌ Image Captioning
๐ฅ ๐ Show and Tell: A Neural Image Caption Generator
๐ฅ ๐ Neural Machine Translation by Jointly Learning to Align and Translate
๐ฅ ๐ StyleNet: Generating Attractive Visual Captions with Styles
๐ฅ ๐ Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
๐ฅ ๐ Where to put the Image in an Image Caption Generator
๐ฅ ๐ Dank Learning: Generating Memes Using Deep Neural Networks
๐ ๐ถโโ๏ธ Object Detection ๐ฆ ๐
๐ฅ ๐ ResNet-Deep Residual Learning for Image Recognition
๐ฅ ๐ YOLO-You Only Look Once: Unified, Real-Time Object Detection
๐ฅ ๐ Microsoft COCO: Common Objects in Context
- COCO dataset
๐ฅ ๐ (R-CNN) Rich feature hierarchies for accurate object detection and semantic segmentation
๐ฅ ๐ Fast R-CNN
- ๐ป Papers with Code
๐ฅ ๐ Faster R-CNN
๐ฅ ๐ Mask R-CNN
๐ ๐ถโโ๏ธ ๐ซ Pose Detection ๐ ๐
๐ฅ ๐ DensePose: Dense Human Pose Estimation In The Wild
๐ฅ ๐ Parsing R-CNN for Instance-Level Human Analysis
๐ก ๐ฃ Deep NLP ๐ฑ ๐ข
๐ฅ ๐ A Primer on Neural Network Models for Natural Language Processing
๐ฅ ๐ Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
๐ฅ ๐ On the Properties of Neural Machine Translation: EncoderโDecoder Approaches
๐ฅ ๐ LSTM: A Search Space Odyssey - by Klaus Greff et al.
๐ฅ ๐ A Critical Review of Recurrent Neural Networksfor Sequence Learning
๐ฅ ๐ Visualizing and Understanding Recurrent Networks
โญ ๐ฅ ๐ Attention Is All You Need โญ
๐ฅ ๐ An Empirical Exploration of Recurrent Network Architectures
๐ฅ ๐ Open AI (GPT-2) Language Models are Unsupervised Multitask Learners
๐ฅ ๐ BERT: Pre-training of Deep Bidirectional Transformers forLanguage Understanding
- Google BERT Annoucement
๐ฅ ๐ Parameter-Efficient Transfer Learning for NLP
๐ฅ ๐ A Sensitivity Analysis of (and Practitionersโ Guide to) ConvolutionalNeural Networks for Sentence Classification
๐ฅ ๐ A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
๐ฅ ๐ Convolutional Neural Networks for Sentence Classification
๐ฅ ๐ Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
๐ฅ ๐ Single Headed Attention RNN: Stop Thinking With Your Head
๐ฅ ๐ Generative Adversarial Nets - Goodfellow et al.
๐ GAN Rabbit Hole -> GAN Papers
โญโโญ GNNs (Graph Neural Networks)
๐ฅ ๐ A Comprehensive Survey on Graph Neural Networks
๐จโโ๏ธ ๐ Medical AI ๐ ๐ฌ
Machine learning classifiers and fMRI: a tutorial overview - by Francisco et al.
๐ Cool Stuff ๐
๐ ๐ SoundNet: Learning Sound Representations from Unlabeled Video
๐จ ๐ CAN: Creative Adversarial NetworksGenerating โArtโ by Learning About Styles andDeviating from Style Norms
๐จ ๐ Deep Painterly Harmonization
- Github Code
๐บ ๐ ๐ Everybody Dance Now
- Everybody Dance Now - Youtube Video
โฝ Soccer on Your Tabletop
๐ฑโโ๏ธ ๐โโ๏ธ ๐ SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
๐ธ ๐ Handheld Mobile Photography in Very Low Light
๐ฏ ๐ ๐ Learning Deep Features for Scene Recognitionusing Places Database
๐ ๐ ๐ High-Speed Tracking withKernelized Correlation Filters
๐ฌ ๐ Recent progress in semantic image segmentation
Rabbit hole -> ๐ ๐ Analytics Vidhya Top 10 Audio Processing Tasks and their papers
:blonde_man: -> ๐ด ๐ ๐ Face Aging With Condintional GANS
:blonde_man: -> ๐ด ๐ ๐ Dual Conditional GANs for Face Aging and Rejuvenation
โ๏ธ ๐ BAGAN: Data Augmentation with Balancing GAN
labml.ai Annotated PyTorch Paper Implementations
๐ฐ Cap Stone Projects ๐ฐ
8 Awesome Data Science Capstone Projects
10 Powerful Applications of Linear Algebra in Data Science
Top 5 Interesting Applications of GANs
Deep Learning Applications a beginner can build in minutes
2019-10-28 Started must-read-papers-for-ml repo
2019-10-29 Added analytics vidhya use case studies article links
2019-10-30 Added Outlier/Anomaly detection paper, separated Boosting, CNN, Object Detection, NLP papers, and added Image captioning papers
2019-10-31 Added Famous Blogs from Deep and Machine Learning Researchers
2019-11-1 Fixed markdown issues, added contribution guideline
2019-11-20 Added Recommender Surveys, and Papers
2019-12-12 Added R-CNN variants, PoseNets, GNNs
2020-02-23 Added GRU paper
Contributors 2
Beginning with machine learning: a comprehensive primer
- Published: 21 July 2021
- Volumeย 230 ,ย pages 2363โ2444, ( 2021 )
Cite this article
- Rahul Yedida 1 &
- Snehanshu Saha 2 ย
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This is a primer on machine learning for beginners. Certainly, there are plenty of excellent books on the subject, providing detailed explanations of many algorithms. The intent of this primer is not to outdo these texts in rigor; rather, to provide an introduction to the subject that is accessible, yet covers all the mathematical details, and provides implementations of most algorithms in Python. We feel this provides a well-rounded understanding of each algorithm: only by writing the code and seeing the math applied, and visually inspecting the algorithmโs working, will a reader be fully able to connect all the dots. The style of the primer is largely conversational, and avoids too much formal jargon. We will certainly introduce all required technical terms, but while explaining an algorithm, we will use simple English and avoid unnecessarily formalisms. We hope this proves useful for individuals willing to seriously study the subject.
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What Is Machine Learning?
A survey on semi-supervised learning
A survey of transfer learning, data availability statement.
This manuscript has associated data in a data repository. [Authorsโ comment: ...].
Blog Link: https://beginningwithml.wordpress.com/ .
https://www.coursera.org/learn/machine-learning .
Image from https://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/ .
https://see.stanford.edu/course/cs229 .
There are other necessary conditions for a matrix to be invertible, but being a square matrix is a fundamental requirement.
This is not, strictly speaking, true. In some cases, the algorithm will perform worse than if the sample was within the range, but in such cases, not scaling would almost certainly not be of help. You could fix this by performing outlier analysis , which aims to find such samples, or by clipping the value to 1, which is a less frequently used approach, but useful in some domains.
Source: https://en.wikipedia.org/wiki/Sigmoid_function .
http://ece.eng.umanitoba.ca/undergraduate/ECE4850T02/Lecture%20Slides/LocallyWeightedRegression.pdf .
We will talk about kernel functions in a lot more detail when we discuss support vector machines. This is just an intuitive understanding of kernels.
https://web.as.uky.edu/statistics/users/pbreheny/621/F10/notes/11-4.pdf .
https://en.wikipedia.org/wiki/Local_regression#Weight_function .
https://www.itl.nist.gov/div898/handbook/pmd/section1/pmd144.htm .
By Inductiveloadโself-made, Mathematica, Inkscape, Public Domain, link: https://commons.wikimedia.org/w/index.php?curid=3817954 .
http://www.cs.princeton.edu/courses/archive/spr09/cos513/scribe/lecture11.pdf .
https://stats.stackexchange.com/a/353342/212844 .
By NicoguaroโOwn work, CC BY 4.0, link: https://commons.wikimedia.org/w/index.php?curid=46259145 .
https://drive.google.com/file/d/1Ngq7t_HxcvVKRRQkrepgtU-P2PaUCYKx/view?usp=sharing .
It is actually pretty friendly; it just has an unfortunate name.
https://math.stackexchange.com/a/602192 .
https://math.stackexchange.com/a/38704 .
Credits: https://www.byclb.com/TR/Tutorials/neural_networks/ch4_1.htm .
Credits: CS229 materials from Stanford SEE.
This specific example is called the duck testโand it is where โduck typing" in Python gets its name.
http://math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf .
Credits: https://bricaud.github.io/personal-blog/entropy-in-decision-trees/ .
https://1drv.ms/b/s!AiFT_8UzfVHdtwT3lwKOb3mF6ssy .
https://drive.google.com/open?id=1BjZrw5_alezgJEpsKgfzSFl0z5fFRq5S .
Machine Learning, 2nd Edition, by Tom M. Mitchell.
Fayyad and Irani, 1991. On the handling of continuous-valued attributes in decision tree generation. http://web.cs.iastate.edu/~honavar/fayyad.pdf .
Fayyad and Irani, 1993. Multi-interval discretization of continuous-valued attributes for classification learning. https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf .
Quinlan, 1986. Induction of decision trees. http://hunch.net/~coms-4771/quinlan.pdf .
https://commons.wikimedia.org/w/index.php?curid=73710028 .
https://xavierbourretsicotte.github.io/SVM_implementation.html .
http://goelhardik.github.io/2016/11/28/svm-cvxopt/ .
https://jonchar.net/notebooks/SVM/ .
https://people.cs.pitt.edu/~milos/courses/cs3750-Fall2007/lectures/class-kernels.pdf .
http://cs229.stanford.edu/notes/cs229-notes3.pdf .
https://www.coursera.org/learn/neural-networks-deep-learning .
https://1drv.ms/b/s!AiFT_8UzfVHdtwgyEQcKNYmIC4v5?e=CSXVdG .
https://1drv.ms/b/s!AiFT_8UzfVHdtwO9luN6QZavlfq-?e=7vCGet .
https://papers.nips.cc/paper/5422-on-the-number-of-linear-regions-of-deep-neural-networks.pdf .
https://arxiv.org/abs/1806.01844 .
https://www.researchgate.net/publication/332513541_Evolution_of_Novel_Activation_Functions_in_Neural_Network_Training_and_implications_in_Habitability_Classification .
https://arxiv.org/abs/1502.01852 .
Srivastava, Nitish, et al. โDropout: a simple way to prevent neural networks from overfitting." The journal of machine learning research 15.1 (2014):1929โ1958.
Ioffe, Sergey, and Christian Szegedy. โBatch normalization: Accelerating deep network training by reducing internal covariate shift.โ arXiv preprint arXiv:1502.03167 (2015).
Santurkar, Shibani, et al. โHow does batch normalization help optimization?.โ Advances in Neural Information Processing Systems. 2018.
Salimans, Tim, and Durk P. Kingma. โWeight normalization: A simple reparameterization to accelerate training of deep neural networks.โ Advances in Neural Information Processing Systems. 2016.
He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." Proceedings of the IEEE international conference on computer vision. 2015.
By StephenekkaโOwn work, CC BY-SA 4.0, Link: https://commons.wikimedia.org/w/index.php?curid=49572625 .
Smith, Leslie N. "A disciplined approach to neural network hyper-parameters: Part 1โlearning rate, batch size, momentum, and weight decay." arXiv preprint arXiv:1803.09820 (2018).
Smith, Leslie N. โCyclical learning rates for training neural networks.โ 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017.
Seong, Sihyeon, et al. โTowards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling.โ UAI. 2018.
Yedida, Rahul, and Snehanshu Saha. โA novel adaptive learning rate scheduler for deep neural networks.โ arXiv preprint arXiv:1902.07399 (2019).
Li, Hao, et al. โVisualizing the loss landscape of neural nets.โ Advances in Neural Information Processing Systems. 2018.
Zeiler, Matthew D., and Rob Fergus. โVisualizing and understanding convolutional networks.โ European conference on computer vision. Springer, Cham, 2014.
Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. โDistilling the knowledge in a neural network.โ arXiv preprint arXiv:1503.02531 (2015).
Furlanello, Tommaso, et al. โBorn again neural networks.โ arXiv preprint arXiv:1805.04770 (2018).
https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148 .
https://blog.floydhub.com/gans-story-so-far/ .
https://1drv.ms/b/s!AiFT_8UzfVHdtwIcgiINLQ-o6sCh?e=49iRq4 .
https://www.coursera.org/specializations/deep-learning .
https://course.fast.ai/ .
https://scikit-learn.org/stable/modules/clustering.html .
https://en.wikipedia.org/wiki/Coordinate_descent .
Image taken from https://stats.stackexchange.com/questions/194734/dbscan-what-is-a-core-point .
Tan, P.N., 2018. Introduction to data mining. Pearson Education India.
https://en.wikipedia.org/wiki/DBSCAN .
Image from https://www.analyticsvidhya.com/blog/2017/02/test-data-scientist-clustering/ .
https://en.wikipedia.org/wiki/Ward%27s_method .
https://newonlinecourses.science.psu.edu/stat505/node/146/ .
From Tan, P.N., 2018. Introduction to data mining. Pearson Education India.
https://en.wikipedia.org/wiki/Graph_partition#Problem .
Bach, F.R. and Jordan, M.I., 2004. Learning spectral clustering. In Advances in neural information processing systems (pp. 305-312).
https://en.wikipedia.org/wiki/Laplacian_matrix .
https://calculatedcontent.com/2012/10/09/spectral-clustering/ .
Ng, A.Y., Jordan, M.I. and Weiss, Y., 2002. On spectral clustering: Analysis and an algorithm. In Advances in neural information processing systems (pp. 849โ856).
https://en.wikipedia.org/wiki/Dunn_index .
see Bernard Desgraupes notes: https://cran.r-project.org/web/packages/clusterCrit/vignettes/clusterCrit.pdf .
https://en.wikipedia.org/wiki/Silhouette_(clustering) .
from L. Kaufman and P. J. Rousseeuw, Finding groups in data: an introduction to cluster analysis, vol. 344. John Wiley & Sons, 2009.
http://cda.psych.uiuc.edu/multivariate_fall_2012/systat_cluster_manual.pdf .
see: https://en.wikipedia.org/wiki/Cophenetic_correlation .
https://en.wikipedia.org/wiki/Rand_index .
https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html .
https://web.archive.org/web/20110124070213/http://gremlin1.gdcb.iastate.edu/MIP/gene/MicroarrayData/gapstatistics.pdf .
https://datasciencelab.wordpress.com/tag/gap-statistic/ .
https://stats.stackexchange.com/a/11702 .
https://en.wikipedia.org/wiki/Jaccard_index .
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Department of Computer Science, North Carolina State University, Raleigh, USA
Rahul Yedida
CSIS and APPCAIR, BITS Pilani K K Birla Goa Campus, Sancoale, India
Snehanshu Saha
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Yedida, R., Saha, S. Beginning with machine learning: a comprehensive primer. Eur. Phys. J. Spec. Top. 230 , 2363โ2444 (2021). https://doi.org/10.1140/epjs/s11734-021-00209-7
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Received : 14 November 2020
Accepted : 22 June 2021
Published : 21 July 2021
Issue Date : September 2021
DOI : https://doi.org/10.1140/epjs/s11734-021-00209-7
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How to Read Research Papers: A Pragmatic Approach for ML Practitioners
Is it necessary for data scientists or machine-learning experts to read research papers?
The short answer is yes. And don’t worry if you lack a formal academic background or have only obtained an undergraduate degree in the field of machine learning.
Reading academic research papers may be intimidating for individuals without an extensive educational background. However, a lack of academic reading experience should not prevent Data scientists from taking advantage of a valuable source of information and knowledge for machine learning and AI development .
This article provides a hands-on tutorial for data scientists of any skill level to read research papers published in academic journals such as NeurIPS , JMLR , ICML, and so on.
Before diving wholeheartedly into how to read research papers, the first phases of learning how to read research papers cover selecting relevant topics and research papers.
Step 1: Identify a topic
The domain of machine learning and data science is home to a plethora of subject areas that may be studied. But this does not necessarily imply that tackling each topic within machine learning is the best option.
Although generalization for entry-level practitioners is advised, I’m guessing that when it comes to long-term machine learning, career prospects, practitioners, and industry interest often shifts to specialization.
Identifying a niche topic to work on may be difficult, but good. Still, a rule of thumb is to select an ML field in which you are either interested in obtaining a professional position or already have experience.
Deep Learning is one of my interests, and I’m a Computer Vision Engineer that uses deep learning models in apps to solve computer vision problems professionally. As a result, I’m interested in topics like pose estimation, action classification, and gesture identification.
Based on roles, the following are examples of ML/DS occupations and related themes to consider.
For this article, I’ll select the topic Pose Estimation to explore and choose associated research papers to study.
Step 2: Finding research papers
One of the most excellent tools to use while looking at machine learning-related research papers, datasets, code, and other related materials is PapersWithCode .
We use the search engine on the PapersWithCode website to get relevant research papers and content for our chosen topic, “Pose Estimation.” The following image shows you how it’s done.
The search results page contains a short explanation of the searched topic, followed by a table of associated datasets, models, papers, and code. Without going into too much detail, the area of interest for this use case is the “Greatest papers with code”. This section contains the relevant papers related to the task or topic. For the purpose of this article, I’ll select the DensePose: Dense Human Pose Estimation In The Wild .
Step 3: First pass (gaining context and understanding)
At this point, we’ve selected a research paper to study and are prepared to extract any valuable learnings and findings from its content.
It’s only natural that your first impulse is to start writing notes and reading the document from beginning to end, perhaps taking some rest in between. However, having a context for the content of a study paper is a more practical way to read it. The title, abstract, and conclusion are three key parts of any research paper to gain an understanding.
The goal of the first pass of your chosen paper is to achieve the following:
- Assure that the paper is relevant.
- Obtain a sense of the paper’s context by learning about its contents, methods, and findings.
- Recognize the author’s goals, methodology, and accomplishments.
The title is the first point of information sharing between the authors and the reader. Therefore, research papers titles are direct and composed in a manner that leaves no ambiguity.
The research paper title is the most telling aspect since it indicates the study’s relevance to your work. The importance of the title is to give a brief perception of the paper’s content.
In this situation, the title is “DensePose: Dense Human Pose Estimation in the Wild.โ This gives a broad overview of the work and implies that it will look at how to provide pose estimations in environments with high levels of activity and realistic situations properly.
The abstract portion gives a summarized version of the paper. It’s a short section that contains 300-500 words and tells you what the paper is about in a nutshell. The abstract is a brief text that provides an overview of the article’s content, researchers’ objectives, methods, and techniques.
When reading an abstract of a machine-learning research paper, you’ll typically come across mentions of datasets, methods, algorithms, and other terms. Keywords relevant to the article’s content provide context. It may be helpful to take notes and keep track of all keywords at this point.
For the paper: “ DensePose: Dense Human Pose Estimation In The Wild “, I identified in the abstract the following keywords: pose estimation, COCO dataset, CNN, region-based models, real-time.
It’s not uncommon to experience fatigue when reading the paper from top to bottom at your first initial pass, especially for Data Scientists and practitioners with no prior advanced academic experience. Although extracting information from the later sections of a paper might seem tedious after a long study session, the conclusion sections are often short. Hence reading the conclusion section in the first pass is recommended.
The conclusion section is a brief compendium of the work’s author or authors and/or contributions and accomplishments and promises for future developments and limitations.
Before reading the main content of a research paper, read the conclusion section to see if the researcher’s contributions, problem domain, and outcomes match your needs.
Following this particular brief first pass step enables a sufficient understanding and overview of the research paper’s scope and objectives, as well as a context for its content. You’ll be able to get more detailed information out of its content by going through it again with laser attention.
Step 4: Second pass (content familiarization)
Content familiarization is a process that’s relevant to the initial steps. The systematic approach to reading the research paper presented in this article. The familiarity process is a step that involves the introduction section and figures within the research paper.
As previously mentioned, the urge to plunge straight into the core of the research paper is not required because knowledge acclimatization provides an easier and more comprehensive examination of the study in later passes.
Introduction
Introductory sections of research papers are written to provide an overview of the objective of the research efforts. This objective mentions and explains problem domains, research scope, prior research efforts, and methodologies.
It’s normal to find parallels to past research work in this area, using similar or distinct methods. Other papers’ citations provide the scope and breadth of the problem domain, which broadens the exploratory zone for the reader. Perhaps incorporating the procedure outlined in Step 3 is sufficient at this point.
Another aspect of the benefit provided by the introduction section is the presentation of requisite knowledge required to approach and understand the content of the research paper.
Graph, diagrams, figures
Illustrative materials within the research paper ensure that readers can comprehend factors that support problem definition or explanations of methods presented. Commonly, tables are used within research papers to provide information on the quantitative performances of novel techniques in comparison to similar approaches.
Generally, the visual representation of data and performance enables the development of an intuitive understanding of the paper’s context. In the Dense Pose paper mentioned earlier, illustrations are used to depict the performance of the author’s approach to pose estimation and create. An overall understanding of the steps involved in generating and annotating data samples.
In the realm of deep learning, it’s common to find topological illustrations depicting the structure of artificial neural networks. Again this adds to the creation of intuitive understanding for any reader. Through illustrations and figures, readers may interpret the information themselves and gain a fuller perspective of it without having any preconceived notions about what outcomes should be.
Step 5: Third pass (deep reading)
The third pass of the paper is similar to the second, though it covers a greater portion of the text. The most important thing about this pass is that you avoid any complex arithmetic or technique formulations that may be difficult for you. During this pass, you can also skip over any words and definitions that you don’t understand or aren’t familiar with. These unfamiliar terms, algorithms, or techniques should be noted to return to later.
During this pass, your primary objective is to gain a broad understanding of what’s covered in the paper. Approach the paper, starting again from the abstract to the conclusion, but be sure to take intermediary breaks in between sections. Moreover, it’s recommended to have a notepad, where all key insights and takeaways are noted, alongside the unfamiliar terms and concepts.
The Pomodoro Technique is an effective method of managing time allocated to deep reading or study. Explained simply, the Pomodoro Technique involves the segmentation of the day into blocks of work, followed by short breaks.
What works for me is the 50/15 split, that is, 50 minutes studying and 15 minutes allocated to breaks. I tend to execute this split twice consecutively before taking a more extended break of 30 minutes. If you are unfamiliar with this time management technique, adopt a relatively easy division such as 25/5 and adjust the time split according to your focus and time capacity.
Step 6: Forth pass (final pass)
The final pass is typically one that involves an exertion of your mental and learning abilities, as it involves going through the unfamiliar terms, terminologies, concepts, and algorithms noted in the previous pass. This pass focuses on using external material to understand the recorded unfamiliar aspects of the paper.
In-depth studies of unfamiliar subjects have no specified time length, and at times efforts span into the days and weeks. The critical factor to a successful final pass is locating the appropriate sources for further exploration.
Unfortunately, there isn’t one source on the Internet that provides the wealth of information you require. Still, there are multiple sources that, when used in unison and appropriately, fill knowledge gaps. Below are a few of these resources.
- The Machine Learning Subreddit
- The Deep Learning Subreddit
- PapersWithCode
- Top conferences such as NIPS , ICML , ICLR
- Research Gate
- Machine Learning Apple
The Reference sections of research papers mention techniques and algorithms. Consequently, the current paper either draws inspiration from or builds upon, which is why the reference section is a useful source to use in your deep reading sessions.
Step 7: Summary (optional)
In almost a decade of academic and professional undertakings of technology-associated subjects and roles, the most effective method of ensuring any new information learned is retained in my long-term memory through the recapitulation of explored topics. By rewriting new information in my own words, either written or typed, I’m able to reinforce the presented ideas in an understandable and memorable manner.
To take it one step further, it’s possible to publicize learning efforts and notes through the utilization of blogging platforms and social media. An attempt to explain the freshly explored concept to a broad audience, assuming a reader isn’t accustomed to the topic or subject, requires understanding topics in intrinsic details.
Undoubtedly, reading research papers for novice Data Scientists and ML practitioners can be daunting and challenging; even seasoned practitioners find it difficult to digest the content of research papers in a single pass successfully.
The nature of the Data Science profession is very practical and involved. Meaning, there’s a requirement for its practitioners to employ an academic mindset, more so as the Data Science domain is closely associated with AI, which is still a developing field.
To summarize, here are all of the steps you should follow to read a research paper:
- Identify A Topic.
- Finding associated Research Papers
- Read title, abstract, and conclusion to gain a vague understanding of the research effort aims and achievements.
- Familiarize yourself with the content by diving deeper into the introduction; including the exploration of figures and graphs presented in the paper.
- Use a deep reading session to digest the main content of the paper as you go through the paper from top to bottom.
- Explore unfamiliar terms, terminologies, concepts, and methods using external resources.
- Summarize in your own words essential takeaways, definitions, and algorithms.
Thanks for reading!
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Journal of Machine Learning Research
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Latest papers
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A Multi-Level Superoptimizer for Tensor Programs
mirage-project/mirage โข 9 May 2024
We introduce Mirage, the first multi-level superoptimizer for tensor programs.
FiT: Flexible Vision Transformer for Diffusion Model
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Machine learning articles from across Nature Portfolio
Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.
โGhost roadsโ could be the biggest direct threat to tropical forests
By using volunteers to map roads in forests across Borneo, Sumatra and New Guinea, an innovative study shows that existing maps of the Asia-Pacific region are rife with errors. It also reveals that unmapped roads are extremely common โ up to seven times more abundant than mapped ones. Such โghost roadsโ are promoting illegal logging, mining, wildlife poaching and deforestation in some of the worldโs biologically richest ecosystems.
Adapting visionโlanguage AI models to cardiology tasks
Visionโlanguage models can be trained to read cardiac ultrasound images with implications for improving clinical workflows, but additional development and validation will be required before such models can replace humans.
- Rima Arnaout
Not every organ ticks the same
A new study describes the development of proteomics-based ageing clocks that calculate the biological age of specific organs and define features of extreme ageing associated with age-related diseases. Their findings support the notion that plasma proteins can be used to monitor the ageing rates of specific organs and disease progression.
- Khaoula Talbi
- Anette Melk
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Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography
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Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification
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SAROS: A dataset for whole-body region and organ segmentation in CT imaging
- Sven Koitka
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MISATO: machine learning dataset of proteinโligand complexes for structure-based drug discovery
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Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF
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Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications
The epitranscriptome holds many unexplored RNA functions, but detecting multiple modifications from one sample remains challenging. Here, authors devise a strategy combining AI and nanopore sequencing to uncover a transcriptome-wide co-occurrence of two modification types in individual RNA molecules.
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Getting Started with Research Papers on Machine Learning: What to Read & How
A quick glance into any of the top-rated research papers on Machine Learning shows us how Machine Learning and digital technologies are becoming an integral part of every industry.
According to recent research by Gartner, “Smart machines will enter mainstream adoption by 2021.” Adopting Machine Learning help your organization gain a major competitive edge.
Why is Machine Learning so Hot Today?
With over 250 million active customers and tens of millions of products, Amazonโs machine learning makes accurate product recommendations. These recommendations are an outcome of the customerโs browsing and purchasing behavior almost instantly. No humans could do that.
Google is using driverless cars with the help of machine learning to make our roads safer. IBMโs Watson is already a big name in healthcare with its machine learning and cognitive computing power.
If you have an interest in a career in Machine Learning or Deep Learning, you must develop a habit of reading Research Papers on Machine Learning regularly. Reading research papers in Machine Learning keeps you abreast of the latest trends and thoughts.
The course books define the basic premises of your learning Research papers on Machine Learning give you a deeper understanding of the implementation models in every industry.
Being an ML professional your primary task is to think about problems that are difficult to identify. Solve them through innovative means, rather than memorize what has already been found.
Another advantage of browsing through research papers on machine learning is that you can learn Machine Learning algorithms better. Students or ML professionals who read research papers on machine learning algorithms have a better understanding of programming and coding.
Want to Know How Machine Learning Is Impacting our Lives?
The food or grocery segment is one area where Machine Learning has left an indelible mark.ย Up to 40% of a grocerโs revenue comes from sales of fresh produce. Therefore, maintaining product quality is very important. But that is easier said than done.
Grocers are dependent on their supply chains and consumers. Keeping their shelves stocked and their products fresh is a difficult situation for them.
But with machine learning grocers already know the secret to smarter fresh-food replenishment. They can train ML programs on historical datasets and input data about promotions and store hours as well. Then use the analyses to gauge how much of each product to order and display.
ML systems can also collect information about weather forecasts, public holidays, order quantity parameters, and other contextual information.
Grocers or store-owners can then issue a recommended order every 24 hours so that the grocer always has the appropriate products in the appropriate amounts in stock.
Research Papers on Machine Learning Algorithms
Research Papers on Machine Learning have questioned which machine learning algorithm and what underlying model structure to use has been based on time-consuming investigations and research by human experts.
It has been found out that the right way to select the best algorithms and the most appropriate model architecture, with the correct hyper-parameters, is through trial and error.
Meta-Learning, as it has evolved through the latest research papers on machine learning. It is a concept where exploration of algorithms and model structures take place using machine learning ย methods.
For us, learning happens at multiple scales. Our brains are born with the ability to learn new concepts and tasks. Similarly, research papers in Machine Learning show that in Meta-Learning or Learning to Learn, there is a hierarchical application of AI algorithms.
This includes first learning which is the best network architecture, and what optimization algorithms and hyper-parameters are most appropriate for the model that has been selected.
The model that has been selected through this process refines the most mundane of tasks. The research has already achieved remarkable results and with the use of different optimization techniques. Evolutionary Strategies is perhaps the best example of this.
However, with a Meta- Reinforcement Learning Algorithm, the objective is to learn the working behind Reinforcement Learning agent that includes both the Reinforcement Learning algorithm and the policy.
Pieter Abbeel gave an explanation for this at the Meta-Learning Symposium held during NIPS 2017. This was also one of the highest rated research papers on Machine Learning.
Research Papers on Machine Learning: One-Shot Learning
In one of the several research papers in Machine Learning, Oriol Vinyals states that humans are capable of learning new concepts with minimal supervision. In a Deep Learning network, there is a requirement of huge amount of labelled training data because neural networks are still not able to recognize a new object that they have only seen once or twice.
However, more recent researches on machine learning have shown that the application of model-based, or metric-based, or optimization-based Meta-Learning approaches to define network architectures that can learn from just a few data examples.
Moreover, the latest research papers on machine learning, i.e., on One-Shot Learning by Vinyals shows significant improvements have taken place over previous baseline one-shot accuracy for video and language tasks.
This approach uses a model that learns a classifier based on an attention kernel to map a small labelled support set and an unlabelled example to its corresponding label
Again, for Reinforcement Learning applications, One-Shot Imitation Learning brings out theย possibility of learning from just a few demonstrations of a given task. It is possible to generalize to new instances of the same task by applying a Meta-Learning approach to train robust policies.
Research Papers on Machine Learning: Simulation-Based Learning
Several existing Reinforcement Learning (RL) systems, today rely on simulations to explore the solution space and solve complex problems. These include systems based on Self-Play for gaming applications.
Self-Play is an essential part of the algorithms used by Google\DeepMind in AlphaGo. In the more recent AlphaGo Zero reinforcement learning systems. These are some of the breakthrough approaches that have defeated the world champion at the ancient Chinese game of Go.
Thus, it is interesting to note that the newer AlphaGo Zero system has achieved a significant step forward. The training of AlphaGo Zero system was entirely by Self-Play RL starting from a completely random play. It received no human data or supervision input. The system is effectively self-learning.
Therefore, simulation for Reinforcement Learning training has also been used in Imagination Augmented RL algorithms โ the recent Imagination-Augmented Agents (I2A) approach improves on the original model-based RL algorithms by combining both model-free and model-based policy rollouts.
Thus, this approach allows the policy improvement & has resulted in a significant improvement in performance.
Research Papers on Machine Learning: The Wasserstein Auto-Encoder
Wasserstein research paper on Auto-Encoders shows how Autoencoders, which are neural networks, are used for dimensionality reduction. Autoencoders are more popularly usedย for generative learning models. Variational autoencoder (VAE) is largely usedย in applications in image and text recognition space.
Moreover, researchers from Max Planck Institute for Intelligent Systems, Germany, in collaboration with scientists from Google Brain have come up with the Wasserstein Auto encoder (WAE). It is capable of utilizing Wasserstein distance in any generative model.
Their aim was to reduce optimal transport cost function in the model distribution.
Thus, after testing, WAE proved to be more functional. It provided a more stable solution than other auto encoders such as VAE with lesser architectural complexity.
Research Papers on Machine Learning: Ultra-strong Machine Learning Comprehensibility of Programs Learned with ILP
Authors of the paper on Ultra-strong machine learning comprehensibility of programs learned with ILPย are among the most widely read research papers on machine learning algorithms . They ย introduced an operational definition for comprehensibility of logic programs. They conducted human trials to determine how properties of a program affect its ease of comprehension.
As a matter of fact, Scholars have used two sets of experiments testing human comprehensibility of logic programs. In the first experiment, they have tested human comprehensibility with and without predicate invention.
Thus, in the second experiment, researchers have directly tested whether any state-of-the-art ILP systems are ultra-strong learners in Michieโs sense, and select the Metagol system for use in human trials.
The results show that participants were not able to learn the relational concept on their own from a set of examples. They were able to apply the relational definition provided by the ILP system correctly.
Moreover, this implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. The scholars are of opinion that improved understanding of this class could have potential relevance to contexts involving human learning, teaching, and verbal interaction.
Develop your Own Thoughts
While all of the aforementioned papers present a unique perspective in the advancements in machine learning, you must develop your own thoughts on a hot topic and publish it.
The novel methods mentioned in these research papers in machine learning provide diverse avenues for ML research. As a Machine Learning and artificial intelligence enthusiasts, you can gain a lot when it comes to the latest techniques developed in research.
Thus, as a researcher, Machine Learning looks promising as a career option. You may go for a course in MOOC or take up online courses like the John Hopkins Data Science specialization.
Thus, participating in Kaggle or other online machine learning competitions will also help you gain experience. Attending local meetups or academic conferences is always a fruitful way to learn.
Career in Data Science
You may also enroll in a Data Analytics course for more lucrative career options in Data Science . Moreover, Industry-relevant curriculums, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya. Need experts for creating a killer resume that stands out in the crowd?
Thus, for a rewarding career in Machine Learning , one must stay up to date with any up and coming changes. This also means staying abreast of the latest developments for tools, theory and algorithms.
Furthermore, online communities are great places to know of these changes. Also, read a lot. Read articles on Google Map-Reduce, Google File System, Google Big Table, and Theย Unreasonable Effectiveness of Data. You will get plenty of free Machine Learning books online. Practice problems, coding competitions, and hackathons are a great way to hone your skills.
Moreover, try finding answers to questions at the end of every research paper on Machine Learning. In addition to research papers in machine learning, subscribe to Machine Learning newsletters or join Machine Learning communities. The latter is better as it helps you gain knowledge through practical implementation of Machine Learning.
Therefore, to build a promising career in Machine Learning, join the Machine Learning Course .
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Related Papers
International Journal of Engineering and Applied Sciences (IJEAS)
Jawed Qureshi
Machine learning is more than just a buzzword. It is fundamentally changing the way that industries and the businesses within them carry out their everyday functions and activities from Finance and Recruitment right the way across to Sales and Marketing experience. Machine learning can be defined as a subset of artificial intelligence (AI) that relies on models and inference to effectively perform a specific task, using algorithms and scientific models. In a more practical sense, a machine learning system takes a set of data and uses it to answer a question and continues to ingest more and more data to teach itself over time and ultimately become able to answer future questions in an unsupervised manner. This paper explores how different industries and organizations are using machine learning algorithms in their day to day activities and where we see this transposed into our own lives.
Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)
There has been a dramatic increase in media interest in Artificial Intelligence (AI), in particular with regards to the promises and potential pitfalls of ongoing research, development and deployments. Recent news of success and failures are discussed. The existential opportunities and threats of extreme goals of AI (expressed in terms of Superintelligence/AGI and SocioEconomic impacts) are examined with regards to this media " frenzy " , and some comment and analysis provided. The application of the paper is in two parts, namely to first provide a review of this media coverage, and secondly to recommend project naming in AI with precise and realistic short term goals of achieving really useful machines, with specific smart components. An example of this is provided, namely the RUMLSM project, a novel AI/Machine Learning system proposed to resolve some of the known issues in bottom-up Deep Learning by Neural Networks, recognised by DARPA as the " Third Wave of AI. " An extensive, and up to date at the time of writing, Internet accessible reference set of supporting media articles is provided.
Machine Learning
Emanuel Diamant
Koteswara Rao Pasupuleti
The machine learning-based artificial intelligence technologies have started a new era of the digital evolution of human civilization. This paper is focused on examining and evaluating the current achievements in machine learning technological implementation and development while identifying prospects in machine learning technologies. Aiming at this target this paper used secondary review as a method of executing this research where various articles, journals, online reports, published books have been used as literature or resources of information.
Geoff T Love
The following is my Capstone project, where I focus on some of the ways machine learning has been incorporated into the lives of billions of people. This paper includes interviews, surveys, and research into the many ways machine learning has influenced the decisions people make everyday.
IOSR Journals
About 20 years back, internet emerged as a great change across the globe and has been ruling the planet since then by making things easier for the people. In the same role we will see machine intelligence over the coming decades. What is the unique, remarkable, self-learning and unpredictable decision making object which is capable of taking unique and apt decisions accord to the situation? It is the human brain. Though there are other brainy species as well, we as humans richly gifted with self-awareness, language, abstract thought, mathematical capability, art, technology, science and so on. The most important aspect is that it is capable of taking decisions on its own without any fixed criteria. Most of the machines which work today depend on software which is fixed with certain algorithms which states clearly what the machine need to do at a particular point and a situation. The machine just follows the instructions of the code and executes them. Till date by using this principle, the technology improved a lot. It led to many innovations which made many things possible which we thought were impossible before. But now there came a stage where things need to get improved further to a next stage for which programming things out is a lengthy and not a good option as we cannot program certain things. We need that technology which can learn things by itself and not confiding on the code for everything. In other words, we need technology which works like a human brain in taking decisions and putting them into action. The technology which the world is trying to improve for this purpose is machine learning. In this paper, it is proposed how machine learning can bring about a major change in technology which in turn helps improving many areas of the world.
pรฉter Gyarmati
This study, Thoughts Concerning Artificial Intelligence & Machine Learning Part II is a continuation of a study published under a similar title and aims to rethink our image of artificial intelligence by taking into account the latest results and technical possibilities. It is timely because receives support that has never been seen before and because the results highly affect our lives. This writing places special emphasis on the fact that artificial intelligence-despite any fantastic expectationsmust be a purposeful activity, which should only be to help man, supporting his work and life. Rather fast development has many gains for the applicants, but at the same time arise many questions about the effect on humankind. This paper states the necessity of continuous revision and shows some points for that. The high speed may cause fast changes and failures, etc. This paper also calls attention to these. In Hungary, the actuality and high demands against this field require nationwide integration. The recently formed and now government-supported AI Coalition with its program also serves this purpose. A workgroup in this Coalition turn its attention to the side effect of the new results of the Artificial Intelligence. The main attention is given to the effect of everyday life and behavior caused by the new prescriptions and regulations. The purpose is to call attention to such anomalies.
Proceedings of the VLDB Endowment
Machine Learning (ML) has become a mature technology that is being applied to a wide range of business problems such as web search, online advertising, product recommendations, object recognition, and so on. As a result, it has become imperative for researchers and practitioners to have a fundamental understanding of ML concepts and practical knowledge of end-to-end modeling. This tutorial takes a hands-on approach to introducing the audience to machine learning. The first part of the tutorial gives a broad overview and discusses some of the key concepts within machine learning. The second part of the tutorial takes the audience through the end-to-end modeling pipeline for a real-world income prediction problem.
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Research Topics & Ideas
Artifical Intelligence (AI) and Machine Learning (ML)
If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.
PS – This is just the start…
We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan ย to fill that gap.
If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .
AI-Related Research Topics & Ideas
Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.
- Developing AI algorithms for early detection of chronic diseases using patient data.
- The use of deep learning in enhancing the accuracy of weather prediction models.
- Machine learning techniques for real-time language translation in social media platforms.
- AI-driven approaches to improve cybersecurity in financial transactions.
- The role of AI in optimizing supply chain logistics for e-commerce.
- Investigating the impact of machine learning in personalized education systems.
- The use of AI in predictive maintenance for industrial machinery.
- Developing ethical frameworks for AI decision-making in healthcare.
- The application of ML algorithms in autonomous vehicle navigation systems.
- AI in agricultural technology: Optimizing crop yield predictions.
- Machine learning techniques for enhancing image recognition in security systems.
- AI-powered chatbots: Improving customer service efficiency in retail.
- The impact of AI on enhancing energy efficiency in smart buildings.
- Deep learning in drug discovery and pharmaceutical research.
- The use of AI in detecting and combating online misinformation.
- Machine learning models for real-time traffic prediction and management.
- AI applications in facial recognition: Privacy and ethical considerations.
- The effectiveness of ML in financial market prediction and analysis.
- Developing AI tools for real-time monitoring of environmental pollution.
- Machine learning for automated content moderation on social platforms.
- The role of AI in enhancing the accuracy of medical diagnostics.
- AI in space exploration: Automated data analysis and interpretation.
- Machine learning techniques in identifying genetic markers for diseases.
- AI-driven personal finance management tools.
- The use of AI in developing adaptive learning technologies for disabled students.
AI & ML Research Topic Ideas (Continued)
- Machine learning in cybersecurity threat detection and response.
- AI applications in virtual reality and augmented reality experiences.
- Developing ethical AI systems for recruitment and hiring processes.
- Machine learning for sentiment analysis in customer feedback.
- AI in sports analytics for performance enhancement and injury prevention.
- The role of AI in improving urban planning and smart city initiatives.
- Machine learning models for predicting consumer behaviour trends.
- AI and ML in artistic creation: Music, visual arts, and literature.
- The use of AI in automated drone navigation for delivery services.
- Developing AI algorithms for effective waste management and recycling.
- Machine learning in seismology for earthquake prediction.
- AI-powered tools for enhancing online privacy and data protection.
- The application of ML in enhancing speech recognition technologies.
- Investigating the role of AI in mental health assessment and therapy.
- Machine learning for optimization of renewable energy systems.
- AI in fashion: Predicting trends and personalizing customer experiences.
- The impact of AI on legal research and case analysis.
- Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
- Machine learning in genomic data analysis for personalized medicine.
- AI-driven algorithms for credit scoring in microfinance.
- The use of AI in enhancing public safety and emergency response systems.
- Machine learning for improving water quality monitoring and management.
- AI applications in wildlife conservation and habitat monitoring.
- The role of AI in streamlining manufacturing processes.
- Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.
Recent AI & ML-Related Studies
While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.
Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies, ย so they can provide some useful insight as to what a research topic looks like in practice.
- An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
- HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
- Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
- Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
- Will digitization, big data, and artificial intelligence โ and deep learningโbased algorithm govern the practice of medicine? (Goh, 2022)
- Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
- Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
- Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
- Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
- Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
- Artificial Intelligence: New Frontiers in RealโTime Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
- Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
- Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (Garcรญa et al., 2022).
- Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
- Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
- Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
- Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
- Machine Learning in Tourism (Rugge, 2022)
- Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
- Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)
As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.ย In the video below, we explore some other important things you’ll need to consider when crafting your research topic.
Get 1-On-1 Help
If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.
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can one come up with their own tppic and get a search
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The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. We propose an alternative approach, IQRef , in which the expert iteratively designs a classifier and the machine helps him or her to determine how well it is performing and, importantly, when to stop, by reporting statistics on a fixed, hold-out sample of annotated records. We justify our approach based on prior work giving a theoretical model of how to re-use hold-out data. We compare the two approaches in the context of identifying a cohort of EHRs and examine their strengths and weaknesses through a case study arising from an optometric research problem. We conclude that both approaches are complementary, and we recommend that they both be employed in conjunction to address the problem of cohort identification in health research.
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2019-10-28 Started must-read-papers-for-ml repo. 2019-10-29 Added analytics vidhya use case studies article links. 2019-10-30 Added Outlier/Anomaly detection paper, separated Boosting, CNN, Object Detection, NLP papers, and added Image captioning papers. 2019-10-31 Added Famous Blogs from Deep and Machine Learning Researchers
This is a primer on machine learning for beginners. Certainly, there are plenty of excellent books on the subject, providing detailed explanations of many algorithms. The intent of this primer is not to outdo these texts in rigor; rather, to provide an introduction to the subject that is accessible, yet covers all the mathematical details, and provides implementations of most algorithms in ...
In this section of the article, we will explore seven of the most beneficial and intriguing research papers that have stood the test of time. 1. ResNet: Research Paper: Deep Residual Learning for Image Recognition. Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Summary:
of the basics of machine learning, it might be better understood as a collection of tools that can be applied to a speci c subset of problems. 1.2 What Will This Book Teach Me? The purpose of this book is to provide you the reader with the following: a framework with which to approach problems that machine learning learning might help solve ...
learning training set machine. hypothesis class. Fig. 2. Machine learning methodology that integrates domain knowl-edge during model selection. Moving beyond the basic formulation described above, machine learning tools can integrate available domain knowledge in the learning process. This is indeed the key to the success of machine learning ...
The abstract is a brief text that provides an overview of the article's content, researchers' objectives, methods, and techniques. When reading an abstract of a machine-learning research paper, you'll typically come across mentions of datasets, methods, algorithms, and other terms. Keywords relevant to the article's content provide context.
International conference on machine learning. PMLR, 2020. So far, all mentioned papers have tackled supervised learning: learning to map X to y. Yet, an entire world is dedicated to a "y-less" world: unsupervised learning. In more detail, this field tackles problems that have no clear answer, yet, useful ones can be obtained.
One of the most excellent tools to use while looking at machine learning-related research papers, datasets, code, and other related materials is PapersWithCode. We use the search engine on the PapersWithCode website to get relevant research papers and content for our chosen topic, "Pose Estimation."
Usually, older papers describe simpler concepts, which is a big plus for you as a beginner. Paper structure: what to skip, what to read. Typical Deep Learning paper has the following structure: Abstract; Introduction; Related Work; Approach in Details; Experiments; Conclusion; References; Structure of a typical Deep Learning paper. Image by ...
The Journal of Machine Learning Research (JMLR), , provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. Final versions are (ISSN 1533-7928) immediately ...
Compared to both open-source and proprietary models, InternVL 1. 5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks. Ranked #6 on Visual Question Answering on MM-Vet. Papers With Code highlights trending Machine Learning research and the code to implement it.
Machine learning articles from across Nature Portfolio. Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers ...
This paper provides 11 handy tips/lessons, equally applicable to machine learning and deep learning. Learning = Representation + Evaluation + Optimization : Representation is choosing the right ...
Meta-Learning, as it has evolved through the latest research papers on machine learning. It is a concept where exploration of algorithms and model structures take place using machine learning methods. For us, learning happens at multiple scales. Our brains are born with the ability to learn new concepts and tasks.
The WHY. In the answer to a question on Quora, asking how to test if one is qualified to pursue a career in Machine Learning, Andrew Ng (founder Google Brain, former head of Baidu AI group) said that anyone is qualified for a career in Machine Learning.He said that after you have completed some ML related courses, "to go even further, read research papers.
In 1959, IBM published a paper in the IBM Journal of Research and Development with an, at the time, obscure and curious title. Authored by IBM's Arthur Samuel, the paper invested the use of machine learning in the game of checkers "to verify the fact that a computer can be programmed so that it will learn to play a better game of checkers ...
Abstract. This article analyzes the basic classification of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It combines analysis on common ...
Most (but not all) of these 20 papers, including the top 8, are on the topic of Deep Learning. However, we see strong diversity - only one author (Yoshua Bengio) has 2 papers, and the papers were published in many different venues: CoRR (3), ECCV (3), IEEE CVPR (3), NIPS (2), ACM Comp Surveys, ICML, IEEE PAMI, IEEE TKDE, Information Fusion, Int ...
Get 1-On-1 Help. If you're still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic. A comprehensive list of research topics ideas in the AI and machine learning area. Includes access to a free webinar ...
A subreddit dedicated to learning machine learning Members Online โข Revanthmk23200. ADMIN MOD Research Papers for beginners . Request I started projects on Deep Learning almost a year ago, I had done a few projects in my own interests. ... Can anyone link me to a repository of beginner and intermediate level research papers to improve my Deep ...
Every year, 1000s of research papers related to Machine Learning are published in popular publications like NeurIPS, ICML, ICLR, ACL, and MLDS. The criteria are using citation counts from three academic sources: scholar.google.com; academic.microsoft.com; and semanticscholar.org. "Key research papers in natural language processing ...
This article examines the background to the problem and outlines a project that TNA undertook to research the feasibility of using commercially available artificial intelligence tools to aid selection. ... this research aims to predict user's personalities based on Indonesian text from social media using machine learning techniques. This ...
A new research paper using LLMs powered robot in physical surgery! Conv-basis paper modify the approximation method to reduce the attention layer computation Vidur framework finds an optimal LLMs deployment configuration Cohere AI published a new study paper to analyze under trained tokens in multiple LLMs
Thoughts and Theory. Learn To Reproduce Papers: Beginner's Guide. Step-by-step instructions on how to understand Deep Learning papers and implement the described approaches. With an example: today we are reproducing a fundamental paper on Image Super-Resolution. Olga Chernytska.
Ohio State University. "New machine learning algorithm promises advances in computing." ScienceDaily. ScienceDaily, 9 May 2024. <www.sciencedaily.com / releases / 2024 / 05 / 240509155536.htm ...