The Risk Management Profile for Artificial Intelligence and Human Rights serves as a practical guide for organisations—including governments, the private sector, and civil society—to design, develop, deploy, use, and govern AI in a manner consistent with respect for international human rights.

This document introduces the effects of population demographics on biometric functions.

YoloV3 Implemented in Tensorflow 2.0

Wild Me's first product, Wildbook supports researchers by allowing collaboration across the globe and automation of photo ID matching

JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.

Diagnose bias in LLMs (Large Language Models) from various points of views, allowing users to choose the most appropriate LLM.

Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀

🧙 A web app to generate template code for machine learning

Deep learning library featuring a higher-level API for TensorFlow.

Interpretability Methods for tf.keras models with Tensorflow 2.x

A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

A repository to quickly generate synthetic data and associated trojaned deep learning models

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

This Responsible AI Governance framework (2024) provides Boards with signposts to high level areas of responsibility and accountability through a checklist of twelve principles which could be at the heart of an AI governance policy. It is for Boards who wish to start their AI journey, or for those who recognize that AI governance may be mission critical since the emergence of generative AI (Gen AI) and large language models (LLMs).

Anekanta® AI’s Privacy Impact Risk Assessment System™ identifies the potential privacy impact risks which arise when using remote biometric AI systems including facial recognition technology. The system creates automated reports which recommended mitigations that consider all risks, including AI governance, human rights, union law, employment rights, privacy rights, impact assessments, prohibitions, voluntary and harmonized standards, and good practice.

Anekanta® AI’s AI Risk Intelligence System™ solves the problem of biometrics and AI system risk classification by challenging the entire AI system lifecycle and providing risk reports which inform compliance requirements in line with the EU AI Act and international regulatory frameworks.

Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

A new one shot face swap approach for image and video domains.