Results 231 to 240 of about 52,458 (341)

Tensor language model enables generative scheduling for efficient tensor compilation. [PDF]

Sci Rep
Mehmood S, Arooj A, Al-Shamayleh AS, Batool S, Ur-Rehman I, Sabir MD, Masood S, Akhunzada A.   +7 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Human-in-the-Loop as a Safety Guardrail: Clinical Accountability in the Large Language Model Era. [PDF]

J Med Internet Res
Zablah I, Molina Y, Garcia-Loureiro A.
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Large-Scale Model-Enhanced Vision-Language Navigation: Recent Advances, Practical Applications, and Future Challenges. [PDF]

Sensors (Basel)
Li Z, Meng X, He X, Zhang Y, Yin W.
europepmc   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference

, 2019
Martin Schmitt, Hinrich Schütze
openalex   +1 more source

Bidirectional Kazakh Sign Language prosody-aware translation using computer vision and speech recognition techniques. [PDF]

Front Artif Intell
Zhassuzak M, Buribayev Z, Aouani M, Yerkos A, Yerimbetova A.   +4 more
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Python probabilistic type inference with natural language support [PDF]

, 2016
Zhaogui Xu, Xiangyu Zhang, Lin Chen, Kexin Pei, Baowen Xu   +4 more
openalex   +1 more source