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awesome-rlhf

A curated list of reinforcement learning with human feedback resources (continually updated)

bonfire

A backend library for multiple instance learning implemented in PyTorch.

bonfire-benchmark

comp3204

COMP3204/COMP6223 Computer Vision (Part II Lecture Materials)

competitivecoding

A repository of all my solutions to coding competitions.

composer

Supercharge Your Model Training

deep-neuroevolution

Deep Neuroevolution

disasterriskcompetition

gym

A toolkit for developing and comparing reinforcement learning algorithms.

heatmap.js

🔥 JavaScript Library for HTML5 canvas based heatmaps

jaearly

kaggleactionsexample

A guide of how to interact with the Kaggle API via GitHub actions.

latextable

A Python library that adds Latex functionality to the Texttable package.

mil-for-non-markovian-reward-modelling

Code for the paper "Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning".

mil-land-cover-classification

Code for the paper "Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification".

mil-manim

Multiple Instance Learning Animations with Manim.

mil-multires-eo

milli

Code for the paper "Model Agnostic Interpretability for Multiple Instance Learning".

miltimeseriesclassification

Inherently Interpretable Time Series Classification via Multiple Instance Learning (MILLET)

obsidianapitutorial

Tutorial mod that uses the Obsidian API.

obsidiansuite

Animator for Minecraft that runs in Minecraft.

online-style-interpolator

An online deep learning image style interpolator.

optuna

A hyperparameter optimization framework

plotneuralnet-mil

Latex code for making Multiple Instance Learning (MIL) neural networks diagrams

pycatch22

python implementation of catch22

pygln

Python implementation of GLN in different frameworks

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-fid

Compute FID scores with PyTorch.

reinforcement_learning_an_introduction_ed2_solutions

My solutions to the exercises in the second edition (2018) of Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.

streaming

A Data Streaming Library for Efficient Neural Network Training