mcbal / deep-implicit-attention
Implementation of deep implicit attention in PyTorch
☆63Updated 3 years ago
Alternatives and similar repositories for deep-implicit-attention:
Users that are interested in deep-implicit-attention are comparing it to the libraries listed below
- Pytorch implementation of the Power Spherical distribution☆74Updated 5 months ago
- Usable implementation of Emerging Symbol Binding Network (ESBN), in Pytorch☆23Updated 4 years ago
- ☆49Updated 4 years ago
- Meta-learning inductive biases in the form of useful conserved quantities.☆37Updated 2 years ago
- Code for the article "What if Neural Networks had SVDs?", to be presented as a spotlight paper at NeurIPS 2020.☆72Updated 5 months ago
- Monotone operator equilibrium networks☆51Updated 4 years ago
- Estimating Gradients for Discrete Random Variables by Sampling without Replacement☆39Updated 4 years ago
- Experiments for Meta-Learning Symmetries by Reparameterization☆56Updated 3 years ago
- Very deep VAEs in JAX/Flax☆46Updated 3 years ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆86Updated 2 years ago
- Riemannian Convex Potential Maps☆67Updated last year
- ☆31Updated 4 years ago
- Code for the Thermodynamic Variational Objective☆26Updated 2 years ago
- ICML 2020 Paper: Latent Variable Modelling with Hyperbolic Normalizing Flows☆53Updated 2 years ago
- [NeurIPS'19] Deep Equilibrium Models Jax Implementation☆38Updated 4 years ago
- Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation☆67Updated 2 years ago
- [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)☆115Updated last year
- Normalizing Flows in Jax☆106Updated 4 years ago
- Official code for UnICORNN (ICML 2021)☆27Updated 3 years ago
- Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'☆25Updated 3 years ago
- Humans understand novel sentences by composing meanings and roles of core language components. In contrast, neural network models for nat…☆27Updated 4 years ago
- General Invertible Transformations for Flow-based Generative Models☆17Updated 4 years ago
- ☆33Updated last year
- Code for "Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations"☆23Updated 2 years ago
- Structured matrices for compressing neural networks☆67Updated last year
- ☆17Updated 3 years ago
- code for "Semi-Discrete Normalizing Flows through Differentiable Tessellation"☆24Updated 2 years ago
- Convex potential flows☆81Updated 3 years ago
- [ICML'21 Oral] Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding☆14Updated 3 years ago
- Official repository for our ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology☆35Updated 3 years ago