A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Xiaoquan Chu, Tanlin Sun, Qian Li, et al.
PSPredictor uses machine learning on sequence features to predict liquid-liquid phase separating proteins, achieving 94.71% cross-validation accuracy.
Charles E. Oestreich, Richard Linares, Ravi Gondhalekar
This paper develops a reinforcement learning policy for six-degree-of-freedom spacecraft docking with rotating targets using proximal policy optimization.
K. Murphy
A comprehensive overview of deep reinforcement learning and sequential decision making, covering value-based, policy-based, model-based, multi-agent, and LLM-related methods.
Eric J. Bigelow, Daniel Wurgaft, YingQiao Wang, et al.
A unified Bayesian model shows that activation steering and in-context learning control LLMs by altering concept priors and accumulating evidence, respectively.
Yi Yu, Liuyi Yao, Yuexiang Xie, et al.
Proposes Agentic Memory (AgeMem), a unified framework integrating long-term and short-term memory management into LLM agent policy via tool-based actions and progressive reinforcement learning.
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.
Zhaohui Yang, Mingzhe Chen, Walid Saad, et al.
Proposes an iterative algorithm to minimize total energy consumption in federated learning over wireless networks under latency constraints, achieving up to 59.5% energy reduction.
Yunlong Lu, Xiaohong Huang, Yueyue Dai, et al.
Integrates federated learning into permissioned blockchain consensus to enable privacy-preserved data sharing for Industrial IoT.
Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, et al.
Comprehensive survey of federated learning applications in IoT, covering services, key applications, challenges, and future directions.
Mingzhe Chen, Zhaohui Yang, Walid Saad, et al.
Proposes a joint learning and communications framework for federated learning over wireless networks, optimizing user selection and resource allocation to minimize FL loss.
Cynthia Dwork, Aaron Roth
This monograph provides a thorough introduction to differential privacy, covering its definition, fundamental techniques, and applications in query-release, mechanism design, and machine learning.
Fabrizio Sebastiani
This survey reviews machine learning approaches to automated text categorization, covering document representation, classifier construction, and evaluation.