A Unified Framework for Dynamics Modeling and Control Design Using Deep Learning With Side Information on Stabilizability [0.03%]
利用有关可稳定性的侧信息的深度学习进行动态建模和控制设计的一个统一框架
Kenji Kashima,Ryota Yoshiuchi,Ran Wang et al.
Kenji Kashima et al.
This article presents a unified framework for dynamics modeling and control design using deep learning, focusing on incorporating prior side information on stabilizability. Control theory provides systematic techniques for designing feedbac...
Diffusion Model-Based Path Follower for a Salamander-Like Robot [0.03%]
基于扩散模型的 salamander 类机器人路径跟踪方法
Zhiang Liu,Yang Liu,Yongchun Fang
Zhiang Liu
Salamander-like robots, renowned for their versatile locomotion, present unique challenges in the development of effective path-following controllers due to their distinctive movement patterns and complex body structures. Conventional path-...
TIENet: A Tri-Interaction Enhancement Network for Multimodal Person Reidentification [0.03%]
TIENet:一种用于多模式行人重识别的三交互增强网络
Xi Yang,Wenjiao Dong,De Cheng et al.
Xi Yang et al.
Multimodal person reidentification (ReID), which aims to learn modality-complementary information by utilizing multimodal images simultaneously for person retrieval, is crucial for achieving all-time and all-weather monitoring. Existing met...
Point-to-Set Metric-Gated Mixture of Experts for Multisource Domain Adaptation Fault Diagnosis [0.03%]
面向多源域适应故障诊断的点到集合度量门控专家混合模型
Boyuan Yang,Jinyuan Zhang,Ruonan Liu et al.
Boyuan Yang et al.
The multisource unsupervised domain adaptation (MUDA) scenario poses a significant challenge in the field of intelligent fault diagnosis (IFD), where the goal is to transfer the knowledge learned from multiple labeled source domains to an u...
Weiqing Yan,Shuochen Yao,Chang Tang et al.
Weiqing Yan et al.
Multiview data, characterized by rich features, are crucial in many machine learning applications. However, effectively extracting intraview features and integrating interview information present significant challenges in multiview learning...
Liang Gao,Li Li,Yingwen Chen et al.
Liang Gao et al.
Federated learning (FL) is a new learning paradigm that enables multiple clients to collaboratively train a high-performance model while preserving user privacy. However, the effectiveness of FL heavily relies on the availability of accurat...
BFCP: Pursue Better Forward Compatibility Pretraining for Few-Shot Class-Incremental Learning [0.03%]
BFCP：追求面向未来的更好兼容预训练以实现few-shot类增量学习
Zhiling Fu,Zhe Wang,Xinlei Xu et al.
Zhiling Fu et al.
Few-shot class-incremental learning (FSCIL) requires learning new knowledge without forgetting old knowledge. Forward compatibility can reserve space for novel classes while maintaining base class knowledge in incremental learning. Better f...
Iterative Reservoir Computing Networks for Reconstructing Irregular Time Series [0.03%]
迭代水库计算网络在重构不规则时间序列中的应用
Yuan-Hung Kuan,Vignesh Narayanan,Jr-Shin Li
Yuan-Hung Kuan
Time series data with missing entries are ubiquitous in a broad spectrum of practical and clinical applications, from climatology and cell biology to personalized medicine. This undesired structure arising either due to undesired artifacts ...
Kaili Xiang,Ruotong Ming,Siyu Chen et al.
Kaili Xiang et al.
The performance of neural network (NN)-driven control systems hinges on the reliability and functionality of the NN unit in the controller. Maintaining the compact set condition for NN training signals (inputs) during operation is crucial f...
Local-Global Structure-Aware Geometric Equivariant Graph Representation Learning for Predicting Protein-Ligand Binding Affinity [0.03%]
局部全局结构感知的几何等变图表示学习在蛋白质配体结合亲和力预测中的应用
Shihong Chen,Haicheng Yi,Zhuhong You et al.
Shihong Chen et al.
Predicting protein-ligand binding affinities is a critical problem in drug discovery and design. A majority of existing methods fail to accurately characterize and exploit the geometrically invariant structures of protein-ligand complexes f...