Results 71 to 80 of about 151,060 (281)

Measuring Catastrophic Forgetting in Visual Question Answering

, 2019
Catastrophic forgetting is a ubiquitous problem for the current generation of Artificial Neural Networks: When a network is asked to learn multiple tasks in a sequence, it fails dramatically as it tends to forget past knowledge.
Plank, Barbara, Fernandez, Raquel, Greco, Claudio, Bernardi, Raffaella   +3 more
core   +1 more source

Translational Barriers and AI‐Driven Challenges of Microfluidics‐Enabled Wearables and Implantable Systems in Personalized Medicine

Advanced Science, EarlyView.
An integrative review of microfluidics‐enabled wearables and implantable systems reveals a single‐track translation pipeline, bridging functional biomaterials with clinical utility. Dynamic feedback loops driven by artificial intelligence advance diagnostics toward personalized closed‐loop theranostics.
Ke Huang, Ching Yin Fong, Xin Huang, Bee Luan Khoo   +3 more
wiley   +1 more source

Mitigating Catastrophic Forgetting in Pest Detection Through Adaptive Response Distillation

Agriculture
Pest detection in agriculture faces the challenge of adapting to new pest species while preserving the ability to recognize previously learned ones. Traditional model fine-tuning approaches often result in catastrophic forgetting, where the acquisition ...
Hongjun Zhang, Zhendong Yin, Dasen Li, Yanlong Zhao   +3 more
doaj   +1 more source

Catastrophic Forgetting in Kolmogorov-Arnold Networks

Proceedings of the AAAI Conference on Artificial Intelligence
Catastrophic forgetting is a longstanding challenge in continual learning, where models lose knowledge from earlier tasks when learning new ones. While various mitigation strategies have been proposed for Multi-Layer Perceptrons (MLPs), recent architectural advances like Kolmogorov-Arnold Networks (KANs) have been suggested to offer intrinsic ...
Mohammad Marufur Rahman, Guanchu Wang, Kaixiong Zhou, Minghan Chen 0001, Fan Yang 0023   +4 more
openaire   +3 more sources

A Facial Foundation Model for Clinical Biomarker Prediction and Real‐World Mobile Deployment

Advanced Science, EarlyView.
MedicalFaceFound is a facial foundation model developed through three‐stage progressive pre‐training, adapting representations from general vision to facial structure and clinical phenotypes. Using a single facial image, it estimates 62 biomarkers across multiple physiological systems and supports downstream risk prediction for cardiovascular ...
Tingfeng Xu, Huixuan Xu, Li Lin, Fei Wang, Yaodong Ding, Shuangqiao Liao, Xingyu Chen, Jichen Wen, Hongqiang Zhao, Jing Hao, Weixiang Zhao, Gong Zhang, Shiguang Shan, Yong Zeng, Hu Han, Minxian Wang   +15 more
wiley   +1 more source

Alleviating Catastrophic Forgetting via Multi-Objective Learning

, 2006
— Handling catastrophic forgetting is an interesting and challenging topic in modeling the memory mechanisms of the human brain using machine learning models.
Yaochu Jin, Bernhard Sendhoff, Sendhoff, B, Jin, Y, Senior Member   +4 more
core   +1 more source

An Expertise Transfer Framework For Autonomous Surgical Assistance

Advanced Science, EarlyView.
This study introduces an expertise transfer framework for procedure‐spanning autonomous surgical assistance. By emulating expert logic through hierarchical perception, attention modeling, and knowledge graph‐based decision‐making, the system provides near‐expert surgical view assistance.
Yuan Gao, Guangdi Chu, Wei Jiao, Yuchuan Zhong, Hang Yuan, Chengjun Li, Jinhua Li, Shuxin Wang, Bo Guan, Jianchang Zhao, Lizhi Pan, Haitao Niu, Jianmin Li   +12 more
wiley   +1 more source

Zero-shot incremental learning using spatial-frequency feature representations

Scientific Reports
Zero-shot incremental learning aims to enable a model to generalize to new classes without forgetting previously learned classes. However, the semantic gap between old and new sample classes can lead to catastrophic forgetting.
Jie Ren, Yang Zhao, Weichuan Zhang, Changming Sun   +3 more
doaj   +1 more source

Overcoming catastrophic forgetting with hard attention to the task

CoRR, 2018
Includes appendix.
Serrà Julià, Joan, Surís, Dídac, Miron, Marius, Karatzoglou, Alexandros   +3 more
openaire   +5 more sources

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source