A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Marc C. Kennedy, Anthony O’Hagan
A Bayesian calibration technique for computer models that accounts for parameter uncertainty and model inadequacy, improving predictions over ad hoc fitting.
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al.
HuBERT uses offline clustering to provide aligned targets for masked prediction, matching or improving wav2vec 2.0 on speech benchmarks.
J. Makhoul
A tutorial review of linear prediction for discrete signals, covering all-pole and pole-zero models, least squares estimation, spectral matching, and applications to data compression.
Priyanka Banerjee, Andreas Philipp Eckert, Anna K. Schrey, et al.
ProTox-II is a freely available webserver that predicts 33 toxicity endpoints using molecular similarity, pharmacophores, fragment propensities, and machine-learning models.
Unknown
Proposes a fast, scalable nonparametric method to quantify uncertainty in predictions from a single deterministic neural network.
Unknown
DOVE is a large-scale, multi-dimensional predictions dataset released to enable more meaningful and generalizable evaluation of large language models.
Unknown
Proposes structured knowledge distillation for semantic segmentation, distilling knowledge per pixel to improve dense prediction.
Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, et al.
A comprehensive survey of multi-task learning for dense prediction tasks, covering architectures, optimization methods, and experimental evaluation across benchmarks.
Wei Li, Rui Shao, Jie He, et al.
UniMPA unifies memory, prediction, and action via an action-grounded transition interface to resolve transition realizability gaps in robotic manipulation.
Md Toufikuzzaman, Md Abul Hassan Samee, M Sohel Rahman
CRISPR-DIPOFF is an interpretable deep learning approach for CRISPR Cas-9 off-target prediction that improves precision-recall balance and reveals two sub-regions in the seed region of sgRNA.
Narges Ahmadi, Yubo Jiao, Jônatas Augusto Manzolli, et al.
This paper proposes a three-agent workflow integrating conversational data collection, structured processing, and LLM-based prediction for travel behavior, achieving 71.5% accuracy with vision-based LLMs.
Shuzhou Yuan, Zhan Qu, A. Kangen, et al.
This paper shows that hallucinations from LLMs, when incorporated into molecule property prediction, can significantly improve predictive accuracy for some models, challenging the view that hallucinations are purely problematic.