no code implementations • 8 Nov 2023 • Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui
Machine learning algorithms minimizing average risk are susceptible to distributional shifts.
no code implementations • 31 May 2023 • Ruimin Gao, Hao Zou, Zhekai Duan
In computer vision, different basic blocks are created around different matrix operations, and models based on different basic blocks have achieved good results.
no code implementations • 24 May 2023 • Hao Zou, Zae Myung Kim, Dongyeop Kang
In NLP, diffusion models have been used in a variety of applications, such as natural language generation, sentiment analysis, topic modeling, and machine translation.
no code implementations • 21 May 2023 • Zimu Wang, Jiashuo Liu, Hao Zou, Xingxuan Zhang, Yue He, Dongxu Liang, Peng Cui
In this work, we focus on exploring two representative categories of heterogeneity in recommendation data that is the heterogeneity of prediction mechanism and covariate distribution and propose an algorithm that explores the heterogeneity through a bilevel clustering method.
1 code implementation • CVPR 2023 • Xingxuan Zhang, Renzhe Xu, Han Yu, Hao Zou, Peng Cui
Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i. e., the worst-case loss within a perturbation radius).
no code implementations • 12 Oct 2022 • Saptarashmi Bandyopadhyay, Shraman Pal, Hao Zou, Abhranil Chandra, Jordan Boyd-Graber
We demonstrate that in a low resource setting, using the generated data improves the QA performance over the baseline system on both NQ and QB data.
1 code implementation • 8 Feb 2022 • Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui, Yong Jiang
In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments.
no code implementations • IEEE International Workshop on Intelligent Robots and Systems (IROS) 2021 • Hao Zou, Xuemeng Yang, Tianxin Huang, Chujuan Zhang, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems.
Ranked #6 on 3D Semantic Scene Completion on SemanticKITTI
1 code implementation • 23 Sep 2021 • Xuemeng Yang, Hao Zou, Xin Kong, Tianxin Huang, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
Specifically, the network takes a raw point cloud as input, and merges the features from the segmentation branch into the completion branch hierarchically to provide semantic information.
Ranked #4 on 3D Semantic Scene Completion on SemanticKITTI
no code implementations • ICCV 2021 • Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, Yong liu
The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline.
no code implementations • NeurIPS 2020 • Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He
Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields.
no code implementations • 22 Oct 2020 • Hao Zou, Jinhao Cui, Xin Kong, Chujuan Zhang, Yong liu, Feng Wen, Wanlong Li
A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates.