Neural networks, deep learning papers

• Overview
  • Deep Learning (2016) Ian Goodfellow, Yoshua Bengio, Aaron Courville
  • Deep Learning in Neural Networks: An Overview (2014) Jurgen Schmidhuber

Feedforward Neural Networks (FNN)

• The perceptron: a probabilistic model for information storage and organization in the brain (1958) F. Rosenblatt
• Multilayer Feedforward Networks are Universal Approximators (1989) K. Hornik
• Deep Big Simple Neural Nets Excel on Hand-written Digit Recognition (2010) Dan Claudiu Cireşan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber
• GMDH Group method of data handling (Website, Wiki)
  • Polynomial Theory of Complex Systems (1971) Ivakhnenko A.G.
  • The Review of Problems Solvable by Algorithms of the Group Method of Data Handling (1995) Ivakhnenko A.G., Ivakhnenko G.A.
• Binarized Neural Networks
  • Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 (2016) Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
  • How to Train a Compact Binary Neural Network with High Accuracy? (2017) Wei Tang, Gang Hua, Liang Wang

Convolutional Neural Networks (CNN)

• One of the papers on convolutional nets - [Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position] (https://www.cs.princeton.edu/courses/archive/spr08/cos598B/Readings/Fukushima1980.pdf) (1980) K. Fukushima
• A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects Zewen Li, Wenjie Yang, Shouheng Peng, Fan Liu
• Flexible, High Performance ConvolutionalNeural Networks for Image Classification (2011) Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhube

Recurrent Neural Networks (RNN)

• Boltzmann machines
  • Learning and relearning in Boltzmann machines (1986) G. E. Hinton, T. J. Sejnowski
• LSTM
  • Long Short-term Memory (1997) S. Hochreiter, J. Schmidhuber
  • Framewise Phoneme Classification withBidirectional LSTM and Other Neural NetworkArchitectures (2005) Alex Graves, Jurgen Schmidhuber

Unsupervised

• Competitive learning
  • Feature Discovery by Competitive Learning (1985) David E. Rumelhart
• Autoencoders
  • Modular learning in neural networks (1987) D.H. Ballard
  • Extracting and composing robust features with denoising autoencoders (2008) P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol
  • From Deep Learning book - Autoencoders (ch. 14) (2016) Ian Goodfellow, Yoshua Bengio, Aaron Courville
  • An Introduction to Variational Autoencoders (2019) Diederik P. Kingma, Max Welling
  • Contractive Auto-Encoders: Explicit Invariance During Feature Extraction (2011) S. Rifai, P. Vincent, X. Muller, X. Glorot, Y. Bengio
  • Deep AutoRegressive Networks (2014) Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra
• Denoising Autoencoders
• VAE Variational autoencoders
  • Auto-Encoding Variational Bayes (2014) Diederik P Kingma, Max Welling
  • Tutorial on Variational Autoencoders (2016) Carl Doersch
  • Variational Autoencoder for Deep Learning of Images, Labels and Captions (2016) Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin
• SOM Self-organizing maps
• Cresceptron (Max-Pooling layers)
  • Cresceptron: A Self-organizing Neural Network Which Grows Adaptively (1992) John (Juyang) Weng, Narendra Ahuja, Thomas S. Huang

Generative Adversarial Networks (GAN)

• Generative Adversarial Networks (2014) Ian J. Goodfellow, Jean Pouget-Abadie∗, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair†, Aaron Courville, Yoshua Bengio
• Time-series Generative Adversarial Networks (2019) J. Yoon, D. Jarrett, M. van der Schaar
• Conditional GAN
  • Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks (2019) A. Koochali, P. Schichtel, S. Ahmed, A. Dengel

Bayesian Neural Networks (BNN)

• A Practical Bayesian Framework for Backpropagation Networks (1992) David J. C. MacKay
• Bayesian Learning for Neural Networks (1995) R.M. Neal
• Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks (1995) David J. C. MacKay
• Practical Variational Inference for Neural Networks (2011) Alex Graves
• Weight Uncertainty in Neural Networks (2015) Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (2016) Y. Gal, Z. Ghahramani
• Stochastic Gradient Descent as Approximate Bayesian Inference (2017) S. Mandt, M.D. Hoffman, D.M. Blei
• Deep neural networks as Gaussian Processes (2018) Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein
• Noisy Natural Gradient as Variational Inference (2018) Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (2018) Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
• Understanding Priors in Bayesian Neural Networks at the Unit Level (2019) Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel
• Bayesian Deep Learning and a Probabilistic Perspective of Generalization (2020) Andrew Gordon Wilson, Pavel Izmailov

Weightless Neural Networks (WNN)

• Based on Random Access Memory (RAM) nodes
• Advances in Weightless Neural Systems (2014) F.M.G. França, M. De Gregorio, P.M.V. Lima, W.R. de Oliveira
• WiSARD
• PLN Probabilistic Logic Nodes
• GSN Goal Seeking Neurons
• GRAM

Activation functions

• Sigmoid
• HardSigmoid
• SiLU, dSiLU
• Tanh, HardTanh
• Softmax
• Softplus
• Softsign
• ReLU Rectified Linear Unit
  • Rectified Linear Units Improve Restricted Boltzmann Machines (2010) V. Nair, G.E. Hinton
  • Deep Sparse Rectifier Neural Networks (2011) X. Glorot, A. Bordes, Y. Bengio
• LReLU Leaky ReLU
  • Rectifier Nonlinearities Improve Neural Network Acoustic Models (2013) A.L. Maas, A.Y. Hannun, A.Y. Ng
• PReLU Parametric ReLU
  • Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (2015) Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
• RReLU Randomized ReLU
  • Empirical Evaluation of Rectified Activations in Convolutional Network (2015) Bing Xu, Naiyan Wang, Tianqi Chen, Mu Li
• SReLU
• ELU
  • Fast and Accurate Deep Network Learning by Exponential Linear Units (2015) Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
• PELU
  • Parametric Exponential Linear Unit forDeep Convolutional Neural Network (2016) L. Trottier, P. Giguère, B. Chaib-draa
• SELU
• Maxout
• Mish
  • Mish: A Self Regularized Non-Monotonic Neural Activation Function (2019) Diganta Misra
• Swish
• ELiSH
• HardELiSH

Inference

• Weight guessing
• Vanishing gradient problem (Wiki)
• Double descent
  • Deep Double Descent: Where Bigger Models and More Data Hurt (2019) Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
• BP Back-propagation
  • Learning representations by back-propagating errors (1986) David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams
  • Backpropagation Applied to Handwritten Zip Code Recognition (1989) Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel
• Pruning - reduces computational cost, improves generalization
  • Optimal Brain Damage (1990) Yann Le Cun, John S. Denker, Sara A. Solla
  • Learning both Weights and Connections for Efficient Neural Networks (2015) Song Han, Jeff Pool, John Tran, William J. Dally
  • Pruning Convolutional Neural Networks for Resource Efficient Inference (2017) Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz
  • Learning Sparse Neural Networks through L0 Regularization (2018) Christos Louizos, Max Welling, Diederik P. Kingma
• Pretraining
  • Why Does Unsupervised Pre-training Help Deep Learning? (2010) D. Erhan, Y. Bengio, A. Courville, P.A. Manzagol, P. Vincent, S. Bengio
• Dropout
  • Improving neural networks by preventing co-adaptation of feature detectors (2012) G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R. R. Salakhutdinov
  • Adaptive dropout for training deep neural networks (2013) L.J. Ba, B. Frey
  • The Dropout Learning Algorithm (2014) P. Baldi, P. Sadowski
  • Fast dropout training (2013) S.I. Wang, C.D. Manning

Compression

• Knowledge Distillation
  • Large neural networks (teacher networks) transfer knowledge to smaller networks (called student networks)
• Neural Network Pruning
  • Removing unimportant weights
• Quantization
  • Reducing the number of bits used to store the weights
• Software
  • KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization (2020) Het Shah, Avishree Khare, Neelay Shah, Khizir Siddiqui

Ensembles

• Neural Network Ensembles (1990) L. K. Hansen, P. Salamon
• When Networks Disagree: Ensemble Methods for Hybrid Neural Networks (1993) M.P. Perrone, L.N. Cooper
• Neural Network Ensembles, Cross Validation, and Active Learning (1995) A. Krogh, J. Vedelsby
• When Ensembling Smaller Models is More Efficient than SingleLarge Models (2020) D. Kondratyuk, M. Tan, M. Brown, B. Gong

Related Topics

• Optimization
• Ensemble Learning
• Bayesian Inference
• Reinforcement Learning
• Interpretability
• NLP
• Computer Vision
• Generative Models
• Graph Neural Networks