ravidziv / Information-bottleneck
Python implementation of the infomration bottleneck method (tishby et al, 1999)
☆36Updated 7 years ago
Related projects ⓘ
Alternatives and complementary repositories for Information-bottleneck
- boundary-seeking generative adversarial networks☆46Updated 6 years ago
- Professor Forcing, NIPS'16☆45Updated 7 years ago
- ☆29Updated 7 years ago
- Replication of the paper "Variational Dropout and the Local Reparameterization Trick" using Lasagne.☆33Updated 7 years ago
- Code for paper "Convergent Learning: Do different neural networks learn the same representations?"☆85Updated 8 years ago
- Proceedings of ICML 2018☆39Updated last year
- Understanding Short-Horizon Bias in Stochastic Meta-Optimization☆37Updated 6 years ago
- Code for "Deep Convolutional Networks as shallow Gaussian Processes"☆38Updated 5 years ago
- TensorFlow implementation of (Momentum) Stochastic Variance-Adapted Gradient.☆44Updated 6 years ago
- Code for "Unifying distillation and privileged information" (ICLR 2016).☆48Updated 8 years ago
- Implementation of the Deep Frank-Wolfe Algorithm -- Pytorch☆61Updated 3 years ago
- Python package to sample from determinantal point processes☆18Updated 9 years ago
- Code for Stochastic Hyperparameter Optimization through Hypernetworks☆23Updated 6 years ago
- ☆78Updated 6 years ago
- Code for "Generative Adversarial Training for Markov Chains" (ICLR 2017 Workshop)☆80Updated 7 years ago
- ☆14Updated 10 years ago
- Deep variational inference in tensorflow☆56Updated 6 years ago
- Tensorflow implementation of convolutional Winner-Take-All Autoencdoer☆29Updated 7 years ago
- Architecture learning for CNN's☆37Updated 7 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- numpy implementation of net 2 net from the paper Net2Net: Accelerating Learning via Knowledge Transfer http://arxiv.org/abs/1511.05641☆53Updated 8 years ago
- D-NTM paper repo☆26Updated 7 years ago
- code for steinGAN - Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning☆26Updated 5 years ago
- Learning Deep Parsimonious Representations, Deep Learning, Clustering, NIPS 2016☆14Updated 4 years ago
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆63Updated 6 years ago
- Code of "Max-margin Deep Generative Models" (NIPS15)☆18Updated 9 years ago
- Contains code relating to this arxiv paper https://arxiv.org/abs/1802.03761☆37Updated 6 years ago
- Code for the paper "Learning sparse transformations through backpropagation"☆42Updated 4 years ago
- ☆38Updated 6 years ago
- On the decision boundary of deep neural networks☆38Updated 6 years ago