Results 81 to 90 of about 21,351 (259)

Application of Decoupled Knowledge Distillation Method in Document-level RelationExtraction [PDF]

Jisuanji kexue
Document-level relation extraction is an important research direction in the field of natural language processing,aiming to extract semantic relationships between entities from unstructured or semi-structured natural language documents.This paper ...
LIU Le, XIAO Rong, YANG Xiao
doaj   +1 more source

Lilrb4a Suppression Reprograms Microglia to Mitigate APOE4‐Associated Amyloid Plaques and Cerebral Amyloid Angiopathy in Association With a PPAR‐Linked Pro‐Clearance State

Advanced Science, EarlyView.
Targeting Lilrb4a in Apolipoprotein E4 (APOE4)‐associated Alzheimer's disease (AD) reprograms microglia toward a beneficial, phagocytic state. Genetic deletion or antisense inhibition of Lilrb4a suppresses p‐SHP2/NF‐κB/STAT1 signaling, restores PPAR‐linked lipid and energy metabolism, and reduces amyloid plaque burden and cerebral amyloid angiopathy ...
Changxu Nie, Ruixi Yang, Xiaotong Wang, Ping Jia, Xueqi Zhang, Yaqi Dai, Xue Bai, Sijia Duan, Yufeng Li, Peng Zheng, Xin Tian, Li Jiang, Chao Wang   +12 more
wiley   +1 more source

Comparative Knowledge Distillation

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
In the era of large scale pretrained models, Knowledge Distillation (KD) serves an important role in transferring the wisdom of computationally heavy teacher models to lightweight, efficient student models while preserving performance. Traditional KD paradigms, however, assume readily available access to teacher models for frequent inference -- a ...
Alex Tianyi Xu, Alex Wilf, Paul Pu Liang, Alexander Obolenskiv, Daniel Fried, Louis-Philippe Morency   +5 more
openaire   +2 more sources

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Dynamic Regulation of Endogenous Transcription Factor Hubs at Single‐Molecule Resolution

Advanced Science, EarlyView.
This study combines single‐molecule microscopy and genome editing to characterize the dynamic behaviors of endogenous oncofusion transcription factor EWS::FLI1 in Ewing sarcoma cells. EWS::FLI1 forms neomorphic hubs that dynamically assemble and dissolve. The hubs are regulated during mitosis, by RNA, and by specific chemicals.
Shawn Yoshida, Yanghao Zhong, Akiko Kumagai, William G. Dunphy, Shasha Chong   +4 more
wiley   +1 more source

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

Leveraging logit uncertainty for better knowledge distillation

Scientific Reports
Knowledge distillation improves student model performance. However, using a larger teacher model does not necessarily result in better distillation gains due to significant architecture and output gaps with smaller student networks. To address this issue,
Zhen Guo, Dong Wang, Qiang He, Pengzhou Zhang   +3 more
doaj   +1 more source

Student-friendly knowledge distillation

Knowledge-Based Systems
In knowledge distillation, the knowledge from the teacher model is often too complex for the student model to thoroughly process. However, good teachers in real life always simplify complex material before teaching it to students. Inspired by this fact, we propose student-friendly knowledge distillation (SKD) to simplify teacher output into new ...
Mengyang Yuan, Bo Lang, Fengnan Quan
openaire   +2 more sources

Large Language Model‐Informed Dual‐Track AI Framework for the Synergistic Design of Crack‐Free and High‐Strength Superalloys

Advanced Science, EarlyView.
This paper illustrates a knowledge‐augmented dual‐track AI framework for advanced superalloy design. First, Large Language Models translate metallurgical heuristics into explicit rules to rapidly prune a vast compositional search space. Subsequently, LLM‐distilled priors safely guide a reinforcement learning agent during autonomous process optimization,
Jian Yao, Junkang Wu, Jie Su, Fuzhu Wang, Zi Wang, Li Wang, Liming Tan, Lan Huang, Feng Liu, Yong Liu   +9 more
wiley   +1 more source

Balance divergence for knowledge distillation

Engineering Applications of Artificial Intelligence
Knowledge distillation has been widely adopted in computer vision task processing, since it can effectively enhance the performance of lightweight student networks by leveraging the knowledge transferred from cumbersome teacher networks. Most existing knowledge distillation methods utilize Kullback-Leibler divergence to mimic the logit output ...
Yafei Qi, Chen Wang 0074, Zhaoning Zhang 0001, Yaping Liu, Yongmin Zhang   +4 more
openaire   +2 more sources