no code implementations • 30 May 2024 • Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu
While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored.
1 code implementation • 8 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.
no code implementations • 4 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.
no code implementations • 20 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.
no code implementations • 16 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).
1 code implementation • 29 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.
1 code implementation • 4 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.
Ranked #2 on 3D Object Detection on Rope3D
no code implementations • 17 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.
no code implementations • 12 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.
1 code implementation • 16 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.
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.
1 code implementation • CVPR 2023 • Han Liu, Yuhao Wu, Shixuan Zhai, Bo Yuan, Ning Zhang
The field of text-to-image generation has made remarkable strides in creating high-fidelity and photorealistic images.