neural-nets
Awesome papers on Neural Networks and Deep Learning
https://github.com/mlpapers/neural-nets
Last synced: 16 days ago
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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
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