no code implementations • 20 Nov 2023 • Xiaotian Han, Quanzeng You, Yongfei Liu, Wentao Chen, Huangjie Zheng, Khalil Mrini, Xudong Lin, Yiqi Wang, Bohan Zhai, Jianbo Yuan, Heng Wang, Hongxia Yang
To mitigate this issue, we manually curate a benchmark dataset specifically designed for MLLMs, with a focus on complex reasoning tasks.
no code implementations • 23 Oct 2023 • Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang, Jundong Li, Fei Wang, Na Zou
Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks.
no code implementations • 1 Oct 2023 • Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Chia-Yuan Chang, Xia Hu
Our method progressively increases the training length throughout the pretraining phase, thereby mitigating computational costs and enhancing efficiency.
no code implementations • 29 Sep 2023 • Xiaotian Han, Hanqing Zeng, Yu Chen, Shaoliang Nie, Jingzhou Liu, Kanika Narang, Zahra Shakeri, Karthik Abinav Sankararaman, Song Jiang, Madian Khabsa, Qifan Wang, Xia Hu
We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.
no code implementations • 23 Sep 2023 • Yicheng Wang, Xiaotian Han, Leisheng Yu, Na Zou
Through our investigation, the discrepancy between the elderly and the young arises due to 1) an imbalanced dataset and 2) the milder symptoms often seen in early-onset patients.
no code implementations • 9 Jul 2023 • Chia-Yuan Chang, Yu-Neng Chuang, Kwei-Herng Lai, Xiaotian Han, Xia Hu, Na Zou
Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias.
1 code implementation • 15 Jun 2023 • Xiaotian Han, Jianfeng Chi, Yu Chen, Qifan Wang, Han Zhao, Na Zou, Xia Hu
This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods.
1 code implementation • 26 Apr 2023 • Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, Xia Hu
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks.
no code implementations • 8 Mar 2023 • Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, Xia Hu
Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23. 37% to 63. 99% for the named entity recognition task and from 75. 86% to 83. 59% for the relation extraction task.
1 code implementation • NeurIPS 2023 • Zhimeng Jiang, Xiaotian Han, Hongye Jin, Guanchu Wang, Rui Chen, Na Zou, Xia Hu
Motivated by these sufficient conditions, we propose robust fairness regularization (RFR) by considering the worst case within the model weight perturbation ball for each sensitive attribute group.
1 code implementation • 31 Jan 2023 • Xiaotian Han, Zhimeng Jiang, Hongye Jin, Zirui Liu, Na Zou, Qifan Wang, Xia Hu
Unfortunately, in this paper, we reveal that the fairness metric $\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: i) zero-value $\Delta DP$ does not guarantee zero violation of demographic parity, ii) $\Delta DP$ values can vary with different classification thresholds.
2 code implementations • 30 Sep 2022 • Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, Neil Shah
Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming.
no code implementations • 27 May 2022 • Yicheng Wang, Xiaotian Han, Chia-Yuan Chang, Daochen Zha, Ulisses Braga-Neto, Xia Hu
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation.
1 code implementation • 15 Feb 2022 • Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu
To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i. e., graphon) of different classes of graphs.
no code implementations • 13 Feb 2022 • Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification.
no code implementations • 8 Feb 2022 • Zhimeng Jiang, Xiaotian Han, Chao Fan, Zirui Liu, Na Zou, Ali Mostafavi, Xia Hu
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i. e., message passing) behind GNNs inducing unfairness issue remains unknown.
no code implementations • 30 Nov 2021 • Xiaotian Han, Quanzeng You, Chunyu Wang, Zhizheng Zhang, Peng Chu, Houdong Hu, Jiang Wang, Zicheng Liu
This dataset provides a more reliable benchmark of multi-camera, multi-object tracking systems in cluttered and crowded environments.
Ranked #2 on
Object Tracking
on MMPTRACK
1 code implementation • ICLR 2022 • Zhimeng Jiang, Xiaotian Han, Chao Fan, Fan Yang, Ali Mostafavi, Xia Hu
We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing.
no code implementations • 29 Sep 2021 • Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu
To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i. e., graphon) of different classes of graphs.
1 code implementation • 27 Jul 2021 • Xiaotian Han, Jianwei Yang, Houdong Hu, Lei Zhang, Jianfeng Gao, Pengchuan Zhang
There is a surge of interest in image scene graph generation (object, attribute and relationship detection) due to the need of building fine-grained image understanding models that go beyond object detection.
1 code implementation • 26 Jun 2020 • Ting-Hsiang Wang, Qingquan Song, Xiaotian Han, Zirui Liu, Haifeng Jin, Xia Hu
To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models.
no code implementations • 14 Sep 2019 • Chuan Shi, Xiaotian Han, Li Song, Xiao Wang, Senzhang Wang, Junping Du, Philip S. Yu
However, the characteristics of users and the properties of items may stem from different aspects, e. g., the brand-aspect and category-aspect of items.
no code implementations • WS 2017 • Song Jiang, Xiaotian Han
In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation.