davidinouye / destructive-deep-learningLinks
Destructive deep learning estimators and functions that are compatible with scikit-learn.
☆18Updated 4 years ago
Alternatives and similar repositories for destructive-deep-learning
Users that are interested in destructive-deep-learning are comparing it to the libraries listed below
Sorting:
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 6 years ago
- Bayesian Backprop RNN implementation pytorch https://arxiv.org/abs/1704.02798☆25Updated 7 years ago
- Experiments for the Neural Autoregressive Flows paper☆125Updated 4 years ago
- Implementation of iterative inference in deep latent variable models☆43Updated 6 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 5 years ago
- TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".☆60Updated 6 years ago
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆49Updated 7 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- PyTorch implementation of AVF☆45Updated 5 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- Computing various norms/measures on over-parametrized neural networks☆49Updated 6 years ago
- Code in PyTorch for the convex combination linear IAF and the Householder Flow, J.M. Tomczak & M. Welling☆91Updated 8 years ago
- The Variational Homoencoder: Learning to learn high capacity generative models from few examples☆34Updated 2 years ago
- ☆117Updated last year
- Scaled MMD GAN☆36Updated 5 years ago
- Understanding normalizing flows☆132Updated 5 years ago
- Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018☆42Updated 6 years ago
- ☆91Updated 6 years ago
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Updated 5 years ago
- [NeurIPS'19] [PyTorch] Adaptive Regularization in NN☆68Updated 5 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"☆121Updated 7 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Implementation of REBAR in PyTorch☆17Updated 7 years ago
- Autoregressive Energy Machines☆78Updated 2 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 5 years ago
- Implementation of the Functional Neural Process models☆42Updated 5 years ago
- Implementation of Conditionally Shifted Neurons by Munkhdalai et al. (https://arxiv.org/pdf/1712.09926.pdf)☆28Updated 7 years ago
- An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU…☆72Updated 7 years ago
- Lagrangian VAE☆28Updated 7 years ago