A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Liyuan Wang, Xingxing Zhang, Hang Su, et al.
A comprehensive survey of continual learning that bridges basic settings, theoretical foundations, representative methods, and practical applications, proposing a unified objective and taxonomy.
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TechRAG proposes an evidence-gated multimodal agentic RAG architecture for technical literature reasoning, motivated by limitations of basic RAG.
Nathan A. Baker, Frank Alexander, T. Bremer, et al.
This workshop report outlines core technologies and basic research needs for scientific machine learning and AI, emphasizing a national strategy for advancing the field.
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This survey traces the evolution of Mixture of Experts (MoE) from its 2013 stacked Deep MoE origins to modern breakthroughs, highlighting automatic specialization and architectural innovations.
Tongtong Feng, Xin Wang, Yu-Gang Jiang, et al.
This paper surveys embodied AI from basics to advances, covering LLM-driven and world model-driven approaches, and proposes a joint MLLM-WM architecture for complex physical tasks.
Richard C. Mohs, Nigel H. Greig
This paper outlines the drug discovery and development process to help basic scientists frame their research for effective translation to clinical use.
Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, et al.
This review summarizes machine learning and deep learning solutions for small data challenges in molecular science, covering both basic and advanced techniques.
Norman P. Jouppi, Cliff Young, Nishant Patil, et al.
Evaluates a custom ASIC (TPU) for neural network inference, showing 15-30x speedup and 30-80x TOPS/Watt over contemporary CPUs and GPUs.
Meetu Jain, Vibha Saihjpal, Narinder Singh, et al.
This paper provides a comprehensive overview of the Particle Swarm Optimization (PSO) algorithm, covering its basic concepts, parameters, and various advances including modifications, extensions, hybridization, and theoretical analysis.
Solveig Badillo, Balázs Bánfai, Fabian Birzele, et al.
This paper introduces foundational machine learning concepts to the pharmacometrics and clinical pharmacology community, bridging basic principles with applications in molecular biology and drug development.