AI-at-MIT / Reading-Groups
MIT MIC Reading Groups
☆16Updated 3 years ago
Alternatives and similar repositories for Reading-Groups:
Users that are interested in Reading-Groups are comparing it to the libraries listed below
- ☆33Updated 4 years ago
- Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" …☆26Updated 2 years ago
- Probabilistic Solution of Differential Equations☆13Updated 2 years ago
- [NeurIPS'19] Deep Equilibrium Models Jax Implementation☆38Updated 4 years ago
- Classic papers from Edwin T. Jaynes converted to latex.☆32Updated 2 years ago
- Visualize neural networks using TikZ in Julia☆12Updated 3 years ago
- A framework for composing Neural Processes in Julia☆76Updated 3 years ago
- This repository contains the Julia code for the paper "Competitive Gradient Descent"☆23Updated 5 years ago
- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.☆51Updated 2 weeks ago
- Modelling epidemiological dynamics and performing inference in these models☆27Updated 3 years ago
- ☆24Updated 5 years ago
- A JAX implementation of stochastic addition.☆12Updated 2 years ago
- Website for the book "The Elements of Differentiable Programming".☆13Updated 4 months ago
- Probabilistic Circuits from the Juice library☆103Updated 7 months ago
- Practical tools for quantifying how well a sample approximates a target distribution☆27Updated 4 years ago
- Code accompanying VarGrad: A Low-Variance Gradient Estimator for Variational Inference☆12Updated 4 years ago
- Causal, Higher-Order, Probabilistic Programming☆164Updated last year
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 3 years ago
- Riemannian Convex Potential Maps☆67Updated last year
- ☆80Updated 3 years ago
- Optimizing PAC-Bayes bounds for Stochastic Neural Networks with Gaussian weights☆27Updated 4 years ago
- A Julia implementation of sparse Gaussian processes via path-wise doubly stochastic variational inference.☆33Updated 4 years ago
- An empirical investigation of deep learning theory☆16Updated 5 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆17Updated 3 years ago
- Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"☆60Updated 2 years ago
- Monotone operator equilibrium networks☆51Updated 4 years ago
- Code to reproduce the results of 👇☆18Updated 2 years ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆86Updated 2 years ago
- Course Website☆9Updated 3 years ago