asteroidhouse / self-tuning-networksLinks
Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088
☆60Updated 6 years ago
Alternatives and similar repositories for self-tuning-networks
Users that are interested in self-tuning-networks are comparing it to the libraries listed below
Sorting:
- TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".☆60Updated 7 years ago
- This repository is no longer maintained. Check☆81Updated 5 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Implementation of iterative inference in deep latent variable models☆43Updated 6 years ago
- Computing various norms/measures on over-parametrized neural networks☆50Updated 7 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 7 years ago
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆49Updated 8 years ago
- Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018☆43Updated 7 years ago
- Hypergradient descent☆147Updated last year
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Updated 5 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆36Updated 5 years ago
- Implementation of the Deep Frank-Wolfe Algorithm -- Pytorch☆63Updated 4 years ago
- rich posterior approximations and anomaly detection☆20Updated 6 years ago
- Implementation of the Functional Neural Process models☆42Updated 5 years ago
- [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling☆41Updated 2 years ago
- ☆46Updated 7 years ago
- [NeurIPS'19] [PyTorch] Adaptive Regularization in NN☆68Updated 6 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆27Updated 6 years ago
- Code for "EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis" https://arxiv.org/abs/1905.05934☆113Updated 5 years ago
- Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"☆121Updated 7 years ago
- Reparameterize your PyTorch modules☆71Updated 5 years ago
- ☆61Updated 2 years ago
- Lua implementation of Entropy-SGD☆81Updated 7 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆42Updated 3 years ago
- ☆35Updated 4 years ago
- ☆27Updated 7 years ago
- PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models☆155Updated 6 years ago
- Geometric Certifications of Neural Nets☆42Updated 3 years ago
- Limitations of the Empirical Fisher Approximation☆49Updated 10 months ago