ml-jku / quamLinks
Quantification of Uncertainty with Adversarial Models
☆30Updated 2 years ago
Alternatives and similar repositories for quam
Users that are interested in quam are comparing it to the libraries listed below
Sorting:
- This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…☆130Updated 2 years ago
- General Invertible Transformations for Flow-based Generative Models☆18Updated 4 years ago
- Repository for the PopulAtion Parameter Averaging (PAPA) paper☆26Updated last year
- Composable kernels for scikit-learn implemented in JAX.☆44Updated 4 years ago
- ☆37Updated 3 years ago
- Official code for the paper: "Metadata Archaeology"☆19Updated 2 years ago
- Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vi…☆67Updated 2 years ago
- Random feature latent variable models in Python☆23Updated 2 years ago
- Official Implementation of the ICML 2023 paper: "Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally …☆72Updated 2 years ago
- ☆192Updated last month
- Meta-learning inductive biases in the form of useful conserved quantities.☆37Updated 2 years ago
- Official repository for our ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology☆36Updated 4 years ago
- Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation☆67Updated 3 years ago
- Experiments for the NeurIPS 2021 paper "Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks"☆13Updated 3 years ago
- Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"☆59Updated 3 years ago
- A library for uncertainty quantification based on PyTorch☆121Updated 3 years ago
- Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inf…☆24Updated 9 months ago
- Unofficial implementation of Conformal Language Modeling by Quach et al☆29Updated 2 years ago
- "How to Trust Your Diffusion Models: A Convex Optimization Approach to Conformal Risk Control"☆18Updated 2 months ago
- Sequence Modeling with Multiresolution Convolutional Memory (ICML 2023)☆125Updated last year
- Bayesian and Maximum Likelihood Implementation of the Normalizing Flow Network (NFN): https://arxiv.org/abs/1907.08982☆21Updated 4 years ago
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆20Updated 2 years ago
- ☆60Updated 3 years ago
- A Python package for generating concise, high-quality summaries of a probability distribution☆53Updated 4 months ago
- Official code for the paper "Compositional Generalization from First Principles" (NeurIPS 2023)☆11Updated 2 years ago
- A library implementing the kernels for and experiments using extrinsic gauge equivariant vector field Gaussian Processes☆25Updated 3 years ago
- Pytorch implementation of SuperPolyak subgradient method.☆43Updated 2 years ago
- Official Implementation of the paper: "A Rate-Distorion View of Uncertainty Quantification", ICML 2024☆28Updated 11 months ago
- Unofficial but Efficient Implementation of "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" in JAX☆85Updated last year
- Tools for working with Long Short-Term Memory (LSTM) networks and sequences in Pytorch☆36Updated 4 years ago