AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ sc…

Open source implementation and extension of Google Research’s PaperBanana for automated academic figures, diagrams, and research visuals, expanded to new domains like slide generation.

🌐 Permanent Hosting Site: http://ai-paper-finder.info/ 🌐 Hugging Face Hosting: https://huggingface.co/spaces/wenhanacademia/ai-paper-finder

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…

List of pathology feature extractors and foundation models

TradingAgents: Multi-Agents LLM Financial Trading Framework

A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

Standardized benchmark for computational pathology foundation models.

Machine Learning in Drug Discovery Resources 2024

Cell2Sentence: Teaching Large Language Models the Language of Biology

dplabtools Python package

Netflix data challenge hosted by PRML course in IITM, we secured 5th position as team Goodfellas

Integrating histology and spatial transcriptomics - NeurIPS 2024

Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data

Code associated to the publication: Scaling self-supervised learning for histopathology with masked image modeling, A. Filiot et al., MedRxiv (2023). We publicly release Phikon 🚀

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

Transparent and reproducible medical image processing pipelines in Python.

Home for the unified VICTRE pipeline for in silico breast imaging.

A curated list for awesome self-supervised learning for graphs.

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

Boiler plate training code, trying to modularize as much as possible

Papers about explainability of GNNs

AAAI 2020 - ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

Collection of resources related with Graph Contrastive Learning.