A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.
A comprehensive survey of efficient LLM serving techniques spanning algorithmic optimizations and system-level designs to address latency and throughput challenges.
Guillermo Gallego, Tobi Delbrück, Garrick Orchard, et al.
A comprehensive survey of event-based vision, covering event camera principles, available sensors, processing algorithms, applications, and future challenges.
Sertaç Karaman, Emilio Frazzoli
Introduces PRM* and RRT*, provably asymptotically optimal sampling-based motion planning algorithms, with analysis via random geometric graphs.
Alain Trémeau, Philippe Colantoni
Presents algorithms combining region adjacency graph and line graph structures to enhance color image segmentation via region growing and watershed transformation.
Aidan P. Thompson, Hasan Metin Aktulga, Richard A. Berger, et al.
This paper serves as the definitive reference for LAMMPS, describing its fundamental algorithms, design strategies for flexibility, and recent capabilities enabled by its extensible architecture.
Berk Hess, Carsten Kutzner, David van der Spoel, et al.
GROMACS 4 introduces algorithmic optimizations and parallelization techniques that achieve high performance and scalability for molecular dynamics simulations.
Bangalore Ravi Kiran, Ibrahim Sobh, Victor Talpaert, et al.
A survey of deep reinforcement learning algorithms for autonomous driving, providing a taxonomy of driving tasks, addressing deployment challenges, and discussing simulators and validation methods.
Brian A. Paden, Michal Čáp, Sze Zheng Yong, et al.
Surveys motion planning and control algorithms for self-driving urban vehicles, comparing approaches by mobility model, environment assumptions, and computational requirements.
Unknown
Proposes a reinforcement learning approach enabling coordination in heterogeneous multi-agent systems where agents may use different learning algorithms and parameters.
Yizhe Chi, Wenyi Li, Deyao Hong, et al.
AI4AI-Bench benchmarks LLM agents on rewriting training algorithms across 10 repositories, finding best agents close only a fifth of the gap to optimal.
Michael Hoy, Alexey S. Matveev, Andrey V. Savkin
This survey reviews collision-free navigation techniques for unmanned vehicles in unknown cluttered environments, focusing on reactive local-sensory-data methods and recent model predictive and sliding mode control laws.
Omer Levy, Yoav Goldberg, Ido Dagan
The paper shows that performance gains of word embeddings over count-based models are due to design choices and hyperparameters, not the algorithms, and these can be transferred to traditional models.