Popular GitHub Repositories for Kronecker Factored Approximation

Uniform Manifold Approximation and Projection

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Differentiable rendering without approximation.

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

Large collection of number systems providing custom arithmetic for mixed-precision algorithm development and optimization for AI, Machine Learning, Computer Vision, Signal Processing, CAE, EDA, control, optimization, estimation, and approximation.

A curated list of mathematics documents ,Concepts, Study Materials , Algorithms and Codes available across the internet for machine learning and deep learning

[AAAI 2021] EMLight: Lighting Estimation via Spherical Distribution Approximation, [TIP] GMLight: Lighting Estimation via Geometric Distribution Approximation

LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation

[IEEE S&P Workshop 2018] "Adversarial Deep Learning for Robust Detection of Binary Encoded Malware" Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, Una-May O’Reilly

Fine-tuning of diffusion models

LoRA (Low-Rank Adaptation) inspector for Stable Diffusion

A* CCG Parser with a Supertag and Dependency Factored Model

[NeurIPS'24] Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy

PyTorch implementation of "BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation"

CSE 571 Artificial Intelligence

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

Factored Cognition Primer: How to write compositional language model programs

Efficient, lightweight variational inference and approximation bounds

PoweFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation