j-min / Dropouts
PyTorch Implementations of Dropout Variants
☆87Updated 7 years ago
Alternatives and similar repositories for Dropouts:
Users that are interested in Dropouts are comparing it to the libraries listed below
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆50Updated 7 years ago
- The Deep Weight Prior, ICLR 2019☆44Updated 3 years ago
- Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation☆68Updated 4 years ago
- OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)☆62Updated last year
- [NeurIPS'19] [PyTorch] Adaptive Regularization in NN☆67Updated 5 years ago
- Implementation of the Sliced Wasserstein Autoencoder using PyTorch☆101Updated 6 years ago
- This repository is no longer maintained. Check☆81Updated 4 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆111Updated 6 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆53Updated 5 years ago
- Implementation of Information Dropout☆39Updated 7 years ago
- Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks☆84Updated 3 years ago
- Unofficial pytorch implementation of a paper, Distributional Smoothing with Virtual Adversarial Training [Miyato+, ICLR2016].☆26Updated 6 years ago
- Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" http…☆62Updated 4 years ago
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020☆235Updated 2 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆63Updated 4 years ago
- This repo provides code used in the paper "Predicting with High Correlation Features" (https://arxiv.org/abs/1910.00164):☆54Updated 5 years ago
- Official code for ICLR 2020 paper "A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning."☆99Updated 4 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- A pytorch implementation for the LSTM experiments in the paper: Why Gradient Clipping Accelerates Training: A Theoretical Justification f…☆44Updated 4 years ago
- Memory efficient MAML using gradient checkpointing☆83Updated 5 years ago
- ☆53Updated 6 years ago
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Updated 4 years ago
- The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) p…☆40Updated 4 years ago
- ☆89Updated 5 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆33Updated 4 years ago
- PyTorch Implementation of Neural Statistician☆60Updated 2 years ago
- Official adversarial mixup resynthesis repository☆35Updated 4 years ago
- Hypergradient descent☆143Updated 7 months ago
- Gold Loss Correction☆86Updated 6 years ago
- Hybrid Discriminative-Generative Training via Contrastive Learning☆75Updated last year