openopt / chop
CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.
☆77Updated 11 months ago
Alternatives and similar repositories for chop:
Users that are interested in chop are comparing it to the libraries listed below
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆81Updated 4 years ago
- Limitations of the Empirical Fisher Approximation☆47Updated 4 years ago
- ☆49Updated 4 years ago
- Hessian spectral density estimation in TF and Jax☆121Updated 4 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 4 years ago
- ☆36Updated 2 years ago
- PyTorch-SSO: Scalable Second-Order methods in PyTorch☆145Updated last year
- ☆53Updated 7 months ago
- Codebase for Learning Invariances in Neural Networks☆93Updated 2 years ago
- Geometric Certifications of Neural Nets☆41Updated 2 years ago
- ☆98Updated 3 years ago
- Autoregressive Energy Machines☆77Updated 2 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated 8 months ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated 9 months ago
- ☆27Updated last year
- Implicit generative models and related stuff based on the MMD, in PyTorch☆16Updated 4 years ago
- ☆80Updated 3 years ago
- Pytorch optimizers implementing Hilbert Constrained Gradient Descent☆19Updated 5 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- This repository contains the Python code to reproduce all the figures and experiments presented in the paper: Masegosa, Andrés. R., Learn…☆9Updated last year
- The Synbols dataset generator is a ServiceNow Research project that was started at Element AI.☆45Updated last year
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 3 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆18Updated 3 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code for Unbiased Implicit Variational Inference (UIVI)☆13Updated 6 years ago
- Statistical adaptive stochastic optimization methods☆33Updated 4 years ago
- Monotone operator equilibrium networks☆51Updated 4 years ago