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McCullough |
Warren |
A logical calculus of the ideas immanent in nervous activity |
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Warren |
A logical calculus of the ideas immanent in nervous activity |
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| 1949 |
Hebb |
Donald |
The Organization of Behaviour |
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| 1950 |
Turing |
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Computing Machinery & Intelligence |
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| 1955 |
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A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence |
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| 1958 |
Rosenblatt |
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The Perceptron: A probabalistic model for information storage and organization in the brain |
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| 1960 |
Fraser |
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Simulation of genetic systems by automatic digital computers |
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| 1960 |
McCarthy |
John |
Recursive functions of symbolic expressions and their computation by machine |
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| 1962 |
Widrow |
Bernard |
Associative Storage and Retrieval of Digital Information in Networks of Adaptive “Neurons” |
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| 1966 |
Papert |
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The Summer Vision Project |
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| 1969 |
Newell |
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An Introduction to Computational Geometry |
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| 1969 |
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Perceptrons |
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| 1970 |
Feigenbaum |
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On Generality and Problem Solving: A Case Study Using the DENDRAL Program |
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| 1971 |
Vapnik |
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On the uniform convergence of relative frequencies of events to their probabilities |
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| 1975 |
Fukushima |
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Cognitron: A self-organizing multilayered neural network |
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| 1976 |
Marr |
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From understanding computation to understanding neural circuitry |
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| 1980 |
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Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position |
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| 1982 |
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Vision: A Computational Investigation into the Human Representation and Processing of Visual Information |
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| 1982 |
Hopfield |
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Neural Networks and Physical Systems with Emergent Collective Computational Abilities |
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| 1984 |
Sutton |
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Temporal Credit Assignment in Reinforcement Learning |
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| 1986 |
Rumelhart |
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Learning representations by back-propagating errors |
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| 1986 |
Hinton |
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Learning representations bty back-propagating errors |
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| 1990 |
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Finding Structure in Time |
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| 1992 |
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Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning |
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| 1994 |
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Measuring the VC-Dimension of a Learning Machine |
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| 1995 |
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Gerald |
Temporal Difference Learning and TD-Gammon |
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| 1995 |
Cortes |
Corinna |
Support-Vector Networks |
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| 1995 |
Vapnick |
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Extracting Support Data for a Given Task |
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| 1997 |
Hochreiter |
Sepp |
Long Short-Term Memory |
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| 1998 |
LeCun |
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Convolutional networks for images, speech, and time-series |
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Robust real-time object detection |
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Pearl |
Judea |
Robustness of Causal Claims |
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Bayesian inference learning |
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Ng |
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Convolutional Deep Belief Networks |
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| 2009 |
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ImageNet: A large-scale hierarchical image database |
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Understanding the difficulty of training deep feedforward neural networks |
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ImageNet Classification with Deep Convolutional Neural Networks ImageNet |
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| 2012 |
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On the importance of initialization and momentum in deep learning |
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| 2014 |
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Generative Adversarial Networks - GANs |
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| 2014 |
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Going Deeper with Convolutions - Inception/GoogleNet |
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| 2014 |
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Machine Learning: The High-Interest Credit Card of Technical Debt |
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| 2014 |
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Sequence to Sequence Learning with Neural Networks |
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| 2014 |
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Show and Tell: A Neural Image Caption Generator |
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Le |
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Neural Architecture Search with Reinforcement Learning |
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| 2014 |
Srivastava |
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting |
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| 2014 |
Ba |
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Adam: A Method for Stochastic Optimization |
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| 2014 |
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How transferable are features in deep neural networks |
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| 2015 |
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Very Deep Convolutional Networks For Large-Scale Image Recognition |
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| 2015 |
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Volodmyr |
Human-level control through deep reinforcement learning |
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| 2015 |
Dean |
Jeff |
Distilling the Knowledge in a Neural Network |
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| 2015 |
Ioffe |
Sergey |
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift |
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| 2015 |
LeCun |
Yann |
Deep Learning |
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| 2016 |
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Joseph |
You only look once: Unified, Real-Time Object Detection |
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| 2016 |
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What’s your ML Test Score? A rubric for ML production systems |
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| 2016 |
Szegudy |
Christian |
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning |
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Silver |
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Mastering the game of Go with deep neural networks and tree search |
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| 2017 |
Lin |
Henry |
Why does deep and cheap learning work so well? |
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| 2017 |
Vaswani |
Ashisk |
Attention is all you need |
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| 2017 |
Silver |
David |
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm |
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| 2018 |
Tolstikhin |
Ilya |
Wasserstein Auto-Encoders |
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| 2018 |
Fedus |
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MaskGAN: Better Text Generation via Filling in the______ |
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| 2019 |
Banburski |
Andrzej |
Theory III: Dynamics and Generalization in Deep Networks - a simple solution |
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Jacob |
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding |
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| 2019 |
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AlphaStar: An evolutionary computation perspective |
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| 2019 |
Winfield |
Alan |
Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems |
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