A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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.
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.
Guanghao Ye, Khiem Pham, Xinzhi Zhang, et al.
Proposes RLSP, a post-training framework that decouples exploration and correctness signals during PPO to enable emergent reasoning behaviors in LLMs.
Adnan El Assadi, Niklas Muennighoff, Jinhyuk Lee
A controlled, cost-aware comparison of LLMs and embedding models across 37 tasks finds them effectively tied in quality, but LLMs cost up to 1,431x more, supporting a division of labor.
Giang Nguyen, Štefan Dlugolinský, Martin Bobák, et al.
This survey provides a comprehensive overview and comparison of Machine Learning and Deep Learning frameworks and libraries for large-scale data mining, highlighting trends and massive parallelism support in the era of Big Data.
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.
Euibeom Shin, Maggie Hartman, Murali Ramanathan
This paper evaluates ChatGPT-4's performance on community pharmacy tasks, finding satisfactory responses for drug information, labeling errors, and decision support, with potential for LLMs in pharmacy practice.
Tiantian Guo, Tongpo Zhang, Eng Gee Lim, et al.
This review traces wavelet theory from construction to applications, highlighting rational wavelet transform advantages and the evolution of Wavelet Neural Networks.
Fabio Galbusera, Gloria Casaroli, Tito Bassani
A narrative review of AI and ML techniques applied to spine research, covering localization, segmentation, diagnosis, outcome prediction, decision support, and ethical issues.
Unknown
This paper proposes a minimalist offline RL approach that avoids out-of-distribution value overestimation by constraining the policy to the data support, achieving strong performance with simple modifications.
Unknown
This survey comprehensively reviews 3D Gaussian Splatting, covering its technologies, challenges, and opportunities for becoming a mainstream 3D representation method.
Unknown
This paper surveys the opportunities and challenges in federated learning, including an extension to vertical federated learning.