A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Mohsen Soori, Behrooz Arezoo, Roza Dastres
This review surveys how AI, ML, and DL are transforming advanced robotics across autonomous navigation, cobots, manufacturing, aviation, and transportation.
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, et al.
A comprehensive survey of uncertainty estimation in deep neural networks, covering sources, modeling approaches, calibration, and practical applications.
Yan Hu, Qingyu Chen, Jingcheng Du, et al.
This paper shows that task-specific prompt engineering, incorporating medical knowledge and few-shot examples, significantly improves GPT-3.5 and GPT-4 performance on clinical NER tasks, though still below BioClinicalBERT.
Ron Graham
This paper establishes worst-case bounds on the slowdown caused by list scheduling anomalies in multiprocessor systems.
Xun Liang, Hanyu Wang, Yezhaohui Wang, et al.
A systematic review of controllable text generation for LLMs, defining core concepts, categorizing tasks, and analyzing methods including model retraining, fine-tuning, and decoding-time intervention.
Unknown
ShieldGemma is a suite of LLM-based safety content moderation models built on Gemma2 that predict safety risks across key harm types in user input and LLM-generated output.
Unknown
SweEval is a cross-lingual safety benchmark that evaluates LLMs on handling swear words across 8 languages, revealing higher vulnerability in Indic languages and transliterated contexts.
Unknown
Introduces Gemma, a family of 2B and 7B state-of-the-art language models derived from Google's Gemini, with advances in language understanding, reasoning, and safety.
Derek C. Angus, Rohan Khera, Tracy Lieu, et al.
This JAMA Summit paper outlines the transformative potential and risks of AI in health care, emphasizing the need for robust evaluation, equity, and infrastructure.
Linjun Li
LLMs can appear safer under direct dangerous objectives than when mediated by other agents, revealing a compositional safety gap.
Unknown
This book provides a comprehensive introduction to foundation models, covering their core concepts, advanced topics, and safety considerations.
Unknown
OS-HARM is a benchmark for measuring the safety of LLM-based computer use agents.