lopezpaz / distillation_privileged_information
Code for "Unifying distillation and privileged information" (ICLR 2016).
☆48Updated 9 years ago
Alternatives and similar repositories for distillation_privileged_information:
Users that are interested in distillation_privileged_information are comparing it to the libraries listed below
- ☆63Updated 8 years ago
- code for steinGAN - Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning☆26Updated 6 years ago
- Code for paper "Convergent Learning: Do different neural networks learn the same representations?"☆86Updated 8 years ago
- Replication of the paper "Variational Dropout and the Local Reparameterization Trick" using Lasagne.☆33Updated 7 years ago
- Lasagne code for weight normalization☆88Updated 9 years ago
- Implementation of auxiliary deep generative models for semi-supervised learning☆28Updated 9 years ago
- Related materials for robust and explainable machine learning☆48Updated 7 years ago
- AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)☆35Updated 6 years ago
- Understanding Short-Horizon Bias in Stochastic Meta-Optimization☆37Updated 7 years ago
- Code for reproducing the results on the MNIST dataset in the paper "Distributional Smoothing with Virtual Adversarial Training"☆110Updated 7 years ago
- Semi-supervised deep learning by metric embedding☆19Updated 8 years ago
- Learning Deep Parsimonious Representations, Deep Learning, Clustering, NIPS 2016☆14Updated 5 years ago
- RNNprop☆36Updated 8 years ago
- Implementation of "Variational Inference for Monte Carlo Objectives"☆21Updated 4 years ago
- Deep variational inference in tensorflow☆56Updated 6 years ago
- Generative moment matching networks☆149Updated 8 years ago
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆63Updated 6 years ago
- A Lasagne and Theano implementation of the paper Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow.☆41Updated 8 years ago
- Code for "How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks"☆101Updated 9 years ago
- Unsupervised Learning by Predicting Noise☆90Updated 3 years ago
- Lua implementation of Entropy-SGD☆81Updated 7 years ago
- ☆29Updated 7 years ago
- Implementation of paper "GibbsNet: Iterative Adversarial Inference for Deep Graphical Models" in PyTorch☆57Updated 7 years ago
- Cluttered MNIST Dataset☆51Updated 10 years ago
- Tensorflow implementation of Adversarial Autoencoders (https://arxiv.org/abs/1511.05644)☆38Updated 7 years ago
- Implementation of Appendix A (Neural Architecture Search with Reinforcement Learning: https://arxiv.org/abs/1611.01578) by chainer☆55Updated 6 years ago
- Demo code for the paper ''Distributional Adversarial Networks''☆19Updated 7 years ago
- ☆68Updated 7 years ago
- Code for Attentive Recurrent Comparators☆57Updated 8 years ago
- Code for "Deep Convolutional Networks as shallow Gaussian Processes"☆39Updated 5 years ago