11 citations · 17 across the 12 of their papers we have counts for
12 papers
Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library
Weixun Wang, Shaopan Xiong, Gengru Chen +38
We introduce ROLL, an efficient, scalable, and user-friendly library designed for Reinforcement Learning Optimization for Large-scale Learning. ROLL caters to three primary user gr…
Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams
Ziqiang Wang, Zhixiang Chi, Yanan Wu +4
Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source. Current methods predomina…
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
Yanan Wu, Jie Liu, Xingyuan Bu +10
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unl…
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Dimitrios Psychogyios, Emanuele Colleoni, Beatrice Van Amsterdam +47
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to de…
Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
Yanan Wu, Zhixiang Chi, Yang Wang +2
Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and…
Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay
Guankun Wang, Long Bai, Yanan Wu +2
Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works hav…