most citedUnicom: Universal and Compact Representation Learning for Image Retrieval

17 citations · 20 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG20232 cited

Federated Linear Contextual Bandits with User-level Differential Privacy

Ruiquan Huang, Huanyu Zhang, Luca Melis +3

This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP). We first introduce a unified federated bandits framework that can a…

physics.app-ph2023

Acoustic meta-stethoscope for cardiac auscultation

Zheng-Ji Chen, Jing-Jing Liu, Bin Liang +2

Straight cylindrical stethoscopes serve as an important alternative to conventional stethoscopes whose application in the treatment of infectious diseases might be limited by the u…

cs.SC2023

Two Variants of Bezout Subresultants for Several Univariate Polynomials

Weidong Wang, Jing Yang

In this paper, we develop two variants of Bezout subresultant formulas for several polynomials, i.e., hybrid Bezout subresultant polynomial and non-homogeneous Bezout subresultant…

cs.CV202317 cited

Unicom: Universal and Compact Representation Learning for Image Retrieval

Xiang An, Jiankang Deng, Kaicheng Yang +5

Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract image-level descriptors. However, the most widely used models are pre-trained on ImageN…

cs.CV20231 cited

Light Sampling Field and BRDF Representation for Physically-based Neural Rendering

Jing Yang, Hanyuan Xiao, Wenbin Teng +2

Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-know…

cs.LG2023

Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs

Yuan Cheng, Ruiquan Huang, Jing Yang +1

In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any gi…