Popular GitHub Repositories for Maximum Entropy Regularization

Private & local AI personal knowledge management app for high entropy people.

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

MiniMax-01 is a simple implementation of the MiniMax algorithm, a widely used strategy for decision-making in two-player turn-based games like Tic-Tac-Toe. The algorithm aims to minimize the maximum possible loss for the player, making it a popular choice for developing AI opponents in various game scenarios.

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

Training neural models with structured signals.

Official implementation of "Segment Any Anomaly without Training via Hybrid Prompt Regularization (SAA+)".

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.

Python library for YOLO small object detection and instance segmentation

Pytorch implementation of High-Fidelity Generative Image Compression + Routines for neural image compression

WhiteLightning distills massive, state-of-the-art language models into lightweight, hyper-efficient text classifiers. It's a command-line tool that lets you create specialized models that run anywhere—from the cloud to the edge—using the universal ONNX format for maximum compatibility.

Implicit Geometric Regularization for Learning Shapes

Simple Implementation of many GAN models with PyTorch.

Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654).

[CVPR 2023] DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models

Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels

A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization, TPAMI 2020

The Entropy Mechanism of Reinforcement Learning for Large Language Model Reasoning.

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Learning kernels to maximize the power of MMD tests