TDLS - Foundational Papers
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Criteria: 1. papers that define a new idea; 2. have a wide impact; 3. field diversity.
Suggestions are welcome!
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TitleYear
Categories
#Citations
Note/why it is important
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Semi-Supervised Classification with Graph Convolutional Networks2017GNN303First successful application of spectral graph convolution
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Inductive Representation Learning on Large Graphs2017GNN156GraphSAGE; inductive learning
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Wide Residual Network2017CV923
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WaveNet: A generative model for raw audio2016NLP, LR441
WaveNet; high quality natural sounding speech generation; causal convolutions
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Perceptual Losses for Real-Time Style Transfer and Super-Resolution2016CV1,177real-time style transfer
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Asynchronous Methods for Deep Reinforcement Learning2016RL982A3C
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You Only Look Once: Unified, Real-Time Object Detection2016CV2,271YOLO, first single-stage object detection with ConvNets
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Mastering the game of Go with Deep Neural Networks & Tree Search2016RL3,694AlphaGo
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Continuous control with deep reinforcement learning2015RL1,283Deep deterministic policy gradient
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Neural Machine Translation by Jointly Learning to Align and Translate2015NLP, LR4,695
Attention. For self attention: Non-local neural networks; for transformers: Attention is all you need
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FlowNet: Learning Optical Flow with Convolutional Networks2015CV248FlowNet
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A Neural Algorithm of Artistic Style2015CV724original style transfer
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Spectral Networks and Locally Connected Networks on Graphs2015GNN359Formulation of spectral graph convolution
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U-Net: Convolutional Networks for Biomedical Image Segmentation2015CV3,225U-Net
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Human-level control through deep reinforcement learning2015RL3,631DQN
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Auto-Encoding Variational Bayes2014LR3,525VAE
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Generative Adversarial Nets2014
CV, LR, RL
5,121GAN; maybe also see the NIPS 2016 tutorial on GANs as a supplement
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Fully Convolutional Networks for Semantic Segmentation2014CV7,316FCN
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Rich feature hierarchies for accurate object detection and semantic segmentation2014CV6,295Original R-CNN (first two-stage object detection with ConvNets)
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Sequence to Sequence Learning with Neural Networks2014NLP4,790seq2seq
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Distributed Representations of Words and Phrases and their Compositionality2013NLP9,627word2vec
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Efficient Estimation of Word Representations in Vector Space2013NLP7,854word2vec
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Representation learning: A review and new perspectives2013LR4,186
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
2013NLP2,478Recursive Neural Tensor Network
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ImageNet Classification with Deep Convolutional Neural Networks2012CV29,479Original ConvNet on ImageNet
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Improving neural networks by preventing co-adaptation of feature detectors2012ML3,390The original dropout
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Particle Swarm Optimization2011ML51,960Biologically inspired optimization
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Wavelets on Graphs via Spectral Graph Theory2011GNN697Fast approximation of spectral graph convolution
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Matrix factorization techniques for recommender systems2009Rec4,860First to use Matrix Factorization for collab. filtering; Netflix Prize winner
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Deep Boltzmann Machines2009ML1,783DBM
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Visualizing Data using t-SNE2008LR5,835t-SNE
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Learning to rank: from pairwise approach to listwise approach2007Rec1,366Learning to rank
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A fast learning algorithm for deep belief nets2006ML9,049
A very fast algorithm far training DBN; still being used for DBN and CNN pretraining
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Diffusion maps2006ML1,905Difussion maps
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A Neural Probabilistic Language Model2003NLP3,866a very significant precursor to the Mikolov papers
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Amazon.com recommendations: item-to-item collaborative filtering2003Rec5,552item2item
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Latent Dirichlet Allocation2003ML, LR24,453LDA
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Random Forrests2001ML39,735Random forrests
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Greedy Function Approximation: A Gradient Boosting Machine2001ML6,114GBM
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Estimating the number of clusters in a data set via the gap statistic2001ML3,283gap statistic for estimating the number of clusters
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Item-based collaborative filtering recommendation algorithms2001Rec7,608item2item
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Policy gradient methods for reinforcement learning with function approximation2000RL1,632policy gradient
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An Overview of Statistical Learning Theory1999ML35,486
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An Introduction to Variational Methods for Graphical Models1999ML2,533
Variation inference; unclear which paper proposed the original VI; this is a highly cited intro paper
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Gradient-based learning applied to document recognition1998CV14,365LeNet
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
1997ML15,989Adaboost
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An empirical study of smoothing techniques for language modeling1996NLP3,289Label smoothing
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Regression Shrinkage and Selection via the Lasso1996ML27,052The lasso
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Support-vector networks1995ML32,501SVM
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a training algorithm for optimal margin classifiers1992ML9,925SVM
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Backpropagation Through Time: What It Does and How to Do It1990NLP, LR3,196BPTT (short)
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The self-organizing map1990ML23,487SOM
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Probablistic Neural Networks1990LR4,194
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Learning from Delayed Rewards (PhD Thesis)1989RL5,963Original Q-learning
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Multilayer feedforward networks are universal approximators1989ML15,932Two-hidden layers as universal approximator
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Approximation by Superpositions of a Sigmoidal Function1989LR10,402Idea that NN with one hidden layer neurons suffices
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A Focused Backpropagation Algorithm for Temporal Pattern Recognition1989NLP, LR306BPTT (long)
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Learning Internal Representations by Error Propagation1986ML25,444Backprop
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Stochastic Estimation of the Maximum of a Regression Function1952ML1,708SGD for ML
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A Stochastic Approximation Method1951ML5,765Stochastic Approximation
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Statistical Modeling: The Two CulturesFrequentists vs. Bayesians
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