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• The perceptron: a probabilistic model for information storage and organization in the brain
• Multilayer Feedforward Networks are Universal Approximators
• Deep Big Simple Neural Nets Excel on Hand-written Digit Recognition
• Website
• Polynomial Theory of Complex Systems
• The Review of Problems Solvable by Algorithms of the Group Method of Data Handling
• Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 - Yaniv, Yoshua Bengio*
• How to Train a Compact Binary Neural Network with High Accuracy?
• A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
• Flexible, High Performance ConvolutionalNeural Networks for Image Classification
• Learning and relearning in Boltzmann machines
• Long Short-term Memory
• Framewise Phoneme Classification withBidirectional LSTM and Other Neural NetworkArchitectures
• Feature Discovery by Competitive Learning
• Modular learning in neural networks
• Extracting and composing robust features with denoising autoencoders
• Autoencoders (ch. 14)
• An Introduction to Variational Autoencoders
• Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
• Deep AutoRegressive Networks
• Auto-Encoding Variational Bayes
• Tutorial on Variational Autoencoders
• Variational Autoencoder for Deep Learning of Images, Labels and Captions
• Cresceptron: A Self-organizing Neural Network Which Grows Adaptively
• Generative Adversarial Networks - Abadie∗, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair†, Aaron Courville, Yoshua Bengio*
• Time-series Generative Adversarial Networks
• Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks
• A Practical Bayesian Framework for Backpropagation Networks
• Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks
• Practical Variational Inference for Neural Networks
• Weight Uncertainty in Neural Networks
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• Stochastic Gradient Descent as Approximate Bayesian Inference
• Deep neural networks as Gaussian Processes - Dickstein*
• Noisy Natural Gradient as Variational Inference
• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
• Understanding Priors in Bayesian Neural Networks at the Unit Level
• Bayesian Deep Learning and a Probabilistic Perspective of Generalization
• Advances in Weightless Neural Systems
• Rectified Linear Units Improve Restricted Boltzmann Machines
• Deep Sparse Rectifier Neural Networks
• Rectifier Nonlinearities Improve Neural Network Acoustic Models
• Empirical Evaluation of Rectified Activations in Convolutional Network
• Fast and Accurate Deep Network Learning by Exponential Linear Units - Arné Clevert, Thomas Unterthiner, Sepp Hochreiter*
• Parametric Exponential Linear Unit forDeep Convolutional Neural Network - draa*
• Mish: A Self Regularized Non-Monotonic Neural Activation Function
• Wiki
• Deep Double Descent: Where Bigger Models and More Data Hurt
• Learning representations by back-propagating errors
• Backpropagation Applied to Handwritten Zip Code Recognition
• Optimal Brain Damage
• Learning both Weights and Connections for Efficient Neural Networks
• Pruning Convolutional Neural Networks for Resource Efficient Inference
• Learning Sparse Neural Networks through L0 Regularization
• Why Does Unsupervised Pre-training Help Deep Learning?
• Improving neural networks by preventing co-adaptation of feature detectors
• Adaptive dropout for training deep neural networks
• The Dropout Learning Algorithm
• Fast dropout training
• KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization
• Neural Network Ensembles
• When Networks Disagree: Ensemble Methods for Hybrid Neural Networks
• Neural Network Ensembles, Cross Validation, and Active Learning
• When Ensembling Smaller Models is More Efficient than SingleLarge Models
• Website
• Polynomial Theory of Complex Systems
• The Review of Problems Solvable by Algorithms of the Group Method of Data Handling
• A Practical Bayesian Framework for Backpropagation Networks
• Stochastic Gradient Descent as Approximate Bayesian Inference
• Backpropagation Applied to Handwritten Zip Code Recognition
• Optimal Brain Damage
• Neural Network Ensembles
• A Practical Bayesian Framework for Backpropagation Networks
• Time-series Generative Adversarial Networks
• A Practical Bayesian Framework for Backpropagation Networks