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Basic Machine Learning and Deep Learning
Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1
[ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning
The MATLAB Agentic Toolkit brings proven MATLAB capabilities to AI agents, making engineering and scientific workflows agent-ready.
Bayesian Program Learning model for one-shot learning
[ICML 2024] TrustLLM: LLM trustworthiness evaluation across truthfulness, safety, fairness, robustness, privacy and ethics. Python/CLI toolkit for local models, compatible APIs and AI agent orchest…
Simplifying robust end-to-end machine learning on Apache Spark.
Codebase for reproducing the experiments of the semantic uncertainty paper (short-phrase and sentence-length experiments).
[NeurIPS 2023] This repository includes the official implementation of our paper "An Inverse Scaling Law for CLIP Training"
Embodied Agent Interface (EAI): Benchmarking LLMs for Embodied Decision Making (NeurIPS D&B 2024 Oral)
This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scal…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Establishing a universal standard of trust between AI agents and merchants for the next phase of agentic commerce.
The fastest Trust Layer for AI Agents
[ICLR 2023] This repository includes the official implementation our paper "Can CNNs Be More Robust Than Transformers?"
[ICLR'26, NAACL'25 Demo] Toolkit & Benchmark for evaluating the trustworthiness of generative foundation models.
[ECCV 2024] Official PyTorch Implementation of "How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs"
Codebase for reproducing the experiments of the semantic uncertainty paper (paragraph-length experiments).