9 citations · 23 across the 15 of their papers we have counts for
15 papers
Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models
Jindi Lv, Aoyu Li, Yuhao Zhou +6
Mamba demonstrates strong efficiency in modeling long visual sequences. However, when token reduction is applied to structurally enhanced Mamba variants, these models exhibit a sev…
ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
Yuhao Zhou, Yunpeng Zhu, Yang Zhou +7
Vision-Language-Action (VLA) models hold great promise for general-purpose robotic intelligence, yet scaling up such models is severely bottlenecked by the high cost of acquiring a…
HyperNAS: Enhancing Architecture Representation for NAS Predictor via Hypernetwork
Jindi Lv, Yuhao Zhou, Yuxin Tian +3
Time-intensive performance evaluations significantly impede progress in Neural Architecture Search (NAS). To address this, neural predictors leverage surrogate models trained on pr…
Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach
Yuhao Zhou, Jindi Lv, Yuxin Tian +3
Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative learning, yet data heterogeneity remains a critical challenge. While existing metho…
GPS: Distilling Compact Memories via Grid-based Patch Sampling for Efficient Online Class-Incremental Learning
Mingchuan Ma, Yuhao Zhou, Jindi Lv +5
Online class-incremental learning aims to enable models to continuously adapt to new classes with limited access to past data, while mitigating catastrophic forgetting. Replay-base…
Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints
Yuhao Zhou, Yuxin Tian, Jindi Lv +5
In the realm of high-frequency data streams, achieving real-time learning within varying memory constraints is paramount. This paper presents Ferret, a comprehensive framework desi…
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing
Yuhao Zhou, Yuxin Tian, Mingjia Shi +4
The exponential growth in model sizes has significantly increased the communication burden in Federated Learning (FL). Existing methods to alleviate this burden by transmitting com…
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Yuxin Tian, Mouxing Yang, Yuhao Zhou +5
Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Wor…
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Mingjia Shi, Yuhao Zhou, Kai Wang +4
Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the perform…
Federated cINN Clustering for Accurate Clustered Federated Learning
Yuhao Zhou, Minjia Shi, Yuxin Tian +3
Federated Learning (FL) presents an innovative approach to privacy-preserving distributed machine learning and enables efficient crowd intelligence on a large scale. However, a sig…
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Yuhao Zhou, Mingjia Shi, Yuanxi Li +3
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. While methods utilizing spars…
Personalized Federated Learning with Hidden Information on Personalized Prior
Mingjia Shi, Yuhao Zhou, Qing Ye +1
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global m…
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg
Yuhao Zhou, Minjia Shi, Yuxin Tian +2
Federated learning (FL) is identified as a crucial enabler for large-scale distributed machine learning (ML) without the need for local raw dataset sharing, substantially reducing…
LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences
Yuhao Zhou, Xihua Li, Yunbo Cao +3
In educational applications, Knowledge Tracing (KT), the problem of accurately predicting students' responses to future questions by summarizing their knowledge states, has been wi…
Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
Yuhao Zhou, Ye Qing, Jiancheng Lv
Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the…