Apprentissage automatique · GitHub

DeepQuantum for quantum computing

• AI-Enhanced Quantum Computing Framework: Seamlessly integrated with PyTorch, it utilizes technologies such as automatic differentiation, vectorized parallelism, and GPU acceleration for efficiency. It facilitates the easy construction of hybrid quantum-classical models, enabling end-to-end training and inference.
• User-Friendly API Design: The API is designed to be simple and intuitive, making it easy to initialize quantum neural networks and providing flexibility in data encoding.

Awesome papers on Language-Model-as-a-Service (LMaaS)

Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

A framework for building provenance-based intrusion detection systems with neural networks (KDD'26, USENIX Sec'25)

Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.

A model-agnostic visual debugging tool for machine learning

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

The official implementation of Self-Play Fine-Tuning (SPIN)

An ML Platform for storing, analyzing, and visualizing your data all in the browser.

🏔️ ALPS: Visual State Encoders for World Models

• 🎯 State. The lowest aggregate LIBERO state-probe error among ViT-B encoders, matching the 3.5x larger V-JEPA 2-L.
• 💬 Language. A native text tower: image-text retrieval within 2 points of the best image-text encoders.

Repo for the Deep Reinforcement Learning Nanodegree program

Setup and customize deep learning environment in seconds.