Search Results for author: Yuhao Wu

Found 11 papers, 6 papers with code

SecGPT: An Execution Isolation Architecture for LLM-Based Systems

1 code implementation8 Mar 2024 Yuhao Wu, Franziska Roesner, Tadayoshi Kohno, Ning Zhang, Umar Iqbal

These LLM apps leverage the de facto natural language-based automated execution paradigm of LLMs: that is, apps and their interactions are defined in natural language, provided access to user data, and allowed to freely interact with each other and the system.

Mitigating Label Noise on Graph via Topological Sample Selection

no code implementations4 Mar 2024 Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu

Despite the success of the carefully-annotated benchmarks, the effectiveness of existing graph neural networks (GNNs) can be considerably impaired in practice when the real-world graph data is noisily labeled.

Learning with noisy labels

BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation

no code implementations20 Feb 2024 Weixin Li, Yuhao Wu, Yang Liu, Weike Pan, Zhong Ming

In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying.

Sequential Recommendation

Online Continual Knowledge Learning for Language Models

no code implementations16 Nov 2023 Yuhao Wu, Tongjun Shi, Karthick Sharma, Chun Wei Seah, Shuhao Zhang

In this paper, we introduce a novel problem in the realm of continual learning: Online Continual Knowledge Learning (OCKL).

Continual Learning Fact Checking +2

SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models

1 code implementation29 Oct 2023 Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai "Helen" Li, Yiran Chen

Specifically, SiDA-MoE attains a remarkable speedup in MoE inference with up to $3. 93\times$ throughput increasing, up to $72\%$ latency reduction, and up to $80\%$ GPU memory saving with down to $1\%$ performance drop.

CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity

1 code implementation4 Oct 2023 Hao Shi, Chengshan Pang, Jiaming Zhang, Kailun Yang, Yuhao Wu, Huajian Ni, Yining Lin, Rainer Stiefelhagen, Kaiwei Wang

Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety.

feature selection Monocular 3D Object Detection +1

Harnessing Scalable Transactional Stream Processing for Managing Large Language Models [Vision]

no code implementations17 Jul 2023 Shuhao Zhang, Xianzhi Zeng, Yuhao Wu, Zhonghao Yang

Large Language Models (LLMs) have demonstrated extraordinary performance across a broad array of applications, from traditional language processing tasks to interpreting structured sequences like time-series data.

Decision Making Management +1

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision

no code implementations12 Jun 2023 Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu

Training a classifier exploiting a huge amount of supervised data is expensive or even prohibited in a situation, where the labeling cost is high.

Binary Classification Pseudo Label

MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

1 code implementation16 Mar 2023 Xuzhe Zhang, Yuhao Wu, Elsa Angelini, Ang Li, Jia Guo, Jerod M. Rasmussen, Thomas G. O'Connor, Pathik D. Wadhwa, Andrea Parolin Jackowski, Hai Li, Jonathan Posner, Andrew F. Laine, Yun Wang

In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation.

Domain Generalization Image Segmentation +5

SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial Examples

1 code implementation CVPR 2023 Han Liu, Yuhao Wu, Zhiyuan Yu, Yevgeniy Vorobeychik, Ning Zhang

LiDAR-based perception is a central component of autonomous driving, playing a key role in tasks such as vehicle localization and obstacle detection.

Autonomous Driving

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