A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Hongyin Zhu
This paper systematically categorizes and answers practical questions about LLMs across five dimensions: computing power, software architecture, data, applications, and brain science.
Muthukuda Arachchige Dona Shiroma Jeeva Shirajanie Niriella
This paper examines AI's integration into Sri Lankan sentencing, highlighting risks of bias and proposing regulatory frameworks for equitable judicial use.
Mona M. Soliman, Eman Ahmed, Ashraf Darwish, et al.
This paper surveys AI integration with emerging technologies in the Metaverse, highlighting benefits like personalized experiences and automation, while addressing challenges such as privacy and bias.
Dawen Zhang, Pamela Finckenberg-Broman, Thong Hoang, et al.
This paper explores the challenges of implementing the Right to be Forgotten in large language models and proposes technical solutions including differential privacy, machine unlearning, model editing, and guardrails.
Shriniket Dixit, Anant Kumar, Kathiravan Srinivasan, et al.
This review explores AI's contributions to improving CRISPR-based genome editing, addressing challenges like off-target effects and delivery, and discussing future research directions.
Xiaofeng Wu, Alan Ritter, Wei Xu
This paper introduces a taxonomy of tabular input representations and table understanding tasks, highlighting critical gaps including retrieval-focused tasks, challenges with complex structures, and limited generalization.
Jens Kober, J. Andrew Bagnell, Jan Peters
A comprehensive survey of reinforcement learning for robot behavior generation, highlighting key challenges, successes, and the trade-offs between model-based/model-free and value-function/policy-search methods.
De Jong Yeong, Gustavo Velasco-Hernandez, John M. Barry, et al.
This paper provides an end-to-end review of sensor hardware, calibration, and fusion techniques for object detection in autonomous vehicles, highlighting challenges and future research directions.
Junfei Qiu, Qihui Wu, Guoru Ding, et al.
This survey reviews machine learning techniques for big data processing, highlighting promising methods and discussing challenges, connections to signal processing, and open research trends.
Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, et al.
A comprehensive survey of machine learning applications across networking domains, covering techniques, challenges, and future research directions.
Kejia Zhang, Youran Sun, Xinyu Ren, et al.
AutoSR automates symbolic regression by searching persistent research states, preserving scientific evidence to recover algebraically equivalent relations across nine benchmark challenges.
Unknown
This review systematically evaluates AI4Science's progress, highlighting the shift from text-centric generation to reasoning-oriented intelligence and its challenges and perspectives.