Results 61 to 70 of about 21,351 (259)
Named Entity Recognition Model Based on k-best Viterbi Decoupling Knowledge Distillation [PDF]
Jisuanji kexue yu tansuoKnowledge distillation is a general approach to improve the performance of the named entity recognition (NER) models. However, the classical knowledge distillation loss functions are coupled, which leads to poor logit distillation.
ZHAO Honglei, TANG Huanling, ZHANG Yu, SUN Xueyuan, LU Mingyu
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CoRR, 2015 Top-performing machine learning systems, such as deep neural networks, large ensembles and complex probabilistic graphical models, can be expensive to store, slow to evaluate and hard to integrate into larger systems. Ideally, we would like to replace such cumbersome models with simpler models that perform equally well.
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Advanced Materials Technologies, EarlyView.This work establishes Bi2MoO6/rGO heterostructures as a mechanism‐guided platform for high‐performance room‐temperature ethanol gas detection by showing that modest adsorption, in conjunction with dynamic interfacial band modulation, enables rapid charge‐transfer.
Sagarika Panda +7 more
wiley +1 more source
IEEE AccessSeismic interpretation is a crucial task in geophysics, requiring accurate prediction of subsurface layer thickness and seismic wave velocity. Traditional methods are computationally intensive and often hindered by noise in seismic data.
Amir Moslemi +4 more
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Similarity Transfer for Knowledge Distillation
CoRR, 2021 Knowledge distillation is a popular paradigm for learning portable neural networks by transferring the knowledge from a large model into a smaller one. Most existing approaches enhance the student model by utilizing the similarity information between the categories of instance level provided by the teacher model.
Haoran Zhao +4 more
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Advanced Materials Technologies, EarlyView.A collagen‐based gut‐on‐chip model replicates the intestinal extracellular matrix, supporting villi‐like structures, tight junctions, and mucin production. This biomimetic platform enables rapid epithelial differentiation and functional barrier formation.
Fernanda López García +4 more
wiley +1 more source
Heterogeneous Knowledge Distillation Using Conceptual Learning
IEEE AccessRecent advances in deep learning have led to the development of large, high-performing models that have been pretrained on massive datasets. However, employing these models in real-world services requires fast inference speed and low computational ...
Yerin Yu, Namgyu Kim
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Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space
Advanced Robotics Research, EarlyView.A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo +6 more
wiley +1 more source
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
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Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source