Popular GitHub Repositories for Entropy Measures

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

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!

Measures and metrics for image2image tasks. PyTorch.

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

A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.

Evaluate your speech-to-text system with similarity measures such as word error rate (WER)

Code and information for face image quality assessment with SER-FIQ

Learn fast, scalable, and calibrated measures of uncertainty using neural networks!

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

The Fast Vector Similarity Library is designed to provide efficient computation of various similarity measures between vectors.

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

DataGene - Identify How Similar TS Datasets Are to One Another (by @firmai)

PyTorch Explain: Interpretable Deep Learning in Python.

[CVPR'20] Implementation for the paper "ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation"

Reference implementation of the paper VERSE: Versatile Graph Embeddings from Similarity Measures

Transformer-XL for Jazz music composition. Paper: "The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-Composed Music through Quantitative Measures", ISMIR 2020

Demystifying Reinforcement Learning in Agentic Reasoning

Cardio chatbot is trained on carefully curated datasets related to cardiovascular diseases. It provides context-aware, medically relevant responses to user queries, supporting both patients and practitioners in understanding symptoms, treatment options, and preventive measures. The model is fine-tuned to stay within the scope of cardiovascular heal

[Materials & Design 2024] A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning

Deepmark AI enables a unique testing environment for language models (LLM) assessment on task-specific metrics and on your own data so your GenAI-powered solution has predictable and reliable performance.