ermongroup / fast_feedforward_computationLinks
Official code for "Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving", ICML 2021
☆29Updated 4 years ago
Alternatives and similar repositories for fast_feedforward_computation
Users that are interested in fast_feedforward_computation are comparing it to the libraries listed below
Sorting:
- [NeurIPS'20] Code for the Paper Compositional Visual Generation and Inference with Energy Based Models☆47Updated 2 years ago
- Code for ICLR 2021 Paper, "Anytime Sampling for Autoregressive Models via Ordered Autoencoding"☆26Updated 2 years ago
- [NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts☆62Updated 3 years ago
- Blog post☆17Updated last year
- Curse-of-memory phenomenon of RNNs in sequence modelling☆19Updated 9 months ago
- The official repository for our paper "The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns …☆16Updated 8 months ago
- ☆33Updated 2 years ago
- ☆44Updated last year
- The Official PyTorch Implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 spotlight…☆57Updated 3 years ago
- ☆14Updated 3 years ago
- Code for "Implicit Normalizing Flows" (ICLR 2021 spotlight)☆37Updated 4 years ago
- ☆63Updated 2 years ago
- [ICML'21] Improved Contrastive Divergence Training of Energy Based Models☆69Updated 3 years ago
- Official code for the paper "Compositional Generalization from First Principles" (NeurIPS 2023)☆13Updated 2 years ago
- Repository for the "Gotta Go Fast When Generating Data with Score-Based Models" paper☆105Updated 4 years ago
- Meta Optimal Transport☆107Updated 2 years ago
- ☆22Updated 4 years ago
- Open source code for paper "On the Learning and Learnability of Quasimetrics".☆32Updated 3 years ago
- Sequence Modeling with Multiresolution Convolutional Memory (ICML 2023)☆127Updated 2 years ago
- Code for ICLR 2023 Paper, "Stable Target Field for Reduced Variance Score Estimation in Diffusion Models”☆76Updated 2 years ago
- A set of tests for evaluating large-scale algorithms for Wasserstein-1 transport computation (NeurIPS'22).☆20Updated last year
- ☆30Updated 5 years ago
- Why Do We Need Weight Decay in Modern Deep Learning? [NeurIPS 2024]☆70Updated last year
- ☆32Updated 2 years ago
- ICLR22 "Fast Differentiable Matrix Square Root" and T-PAMI extension☆67Updated last year
- Code for GFlowNet-EM, a novel algorithm for fitting latent variable models with compositional latents and an intractable true posterior.☆42Updated 2 years ago
- ☆24Updated 4 years ago
- ☆53Updated 4 years ago
- ☆64Updated 2 years ago
- NF-Layers for constructing neural functionals.☆93Updated 2 years ago