activity
20232026
most citedRethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting

24 citations · 24 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning

Xinyu Luo, Hui Liu, Yihua Shao +3

On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference. This retrieval must exploit tas…

cs.LG2025

Efficient Test-Time Adaptation through Latent Subspace Coefficients Search

Xinyu Luo, Jie Liu, Kecheng Chen +4

Real-world deployment often exposes models to distribution shifts, making test-time adaptation (TTA) critical for robustness. Yet most TTA methods are unfriendly to edge deployment…

cs.AR2024

Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks

Junyi Yang, Ruibin Mao, Mingrui Jiang +9

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using p…

cs.LG2023

Target-agnostic Source-free Domain Adaptation for Regression Tasks

Tianlang He, Zhiqiu Xia, Jierun Chen +2

Unsupervised domain adaptation (UDA) seeks to bridge the domain gap between the target and source using unlabeled target data. Source-free UDA removes the requirement for labeled s…

cs.AI202324 cited

Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting

Qian Ma, Zijian Zhang, Xiangyu Zhao +5

With the acceleration of urbanization, traffic forecasting has become an essential role in smart city construction. In the context of spatio-temporal prediction, the key lies in ho…