no code implementations • 10 Mar 2024 • Xunpeng Huang, Hanze Dong, Difan Zou, Tong Zhang
Along this line, Freund et al. (2022) suggest that the modified Langevin algorithm with prior diffusion is able to converge dimension independently for strongly log-concave target distributions.
no code implementations • 12 Jan 2024 • Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang
Specifically, DMC follows the reverse SDE of a diffusion process that transforms the target distribution to the standard Gaussian, utilizing a non-parametric score estimation.
1 code implementation • 5 Jan 2024 • Renjie Pi, Tianyang Han, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang
The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs.
1 code implementation • 18 Dec 2023 • Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang
We investigate its behavior in three distinct settings -- offline, online, and hybrid -- and propose efficient algorithms with finite-sample theoretical guarantees.
no code implementations • 29 Sep 2023 • Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, Tong Zhang
Contrary to the conventional wisdom that focuses on learning invariant features for better OOD performance, our findings suggest that incorporating a large number of diverse spurious features weakens their individual contributions, leading to improved overall OOD generalization performance.
no code implementations • 12 Sep 2023 • Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan YAO, Tong Zhang
Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers.
no code implementations • 5 Jul 2023 • Xunpeng Huang, Hanze Dong, Yifan Hao, Yi-An Ma, Tong Zhang
We propose a Monte Carlo sampler from the reverse diffusion process.
1 code implementation • 21 Jun 2023 • Shizhe Diao, Rui Pan, Hanze Dong, Ka Shun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang
As the number of available foundation models and specialized tasks keeps growing, the job of training scientific language models becomes highly nontrivial.
1 code implementation • 23 May 2023 • Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang
Overall, our proposed paradigm and DetGPT demonstrate the potential for more sophisticated and intuitive interactions between humans and machines.
2 code implementations • 13 Apr 2023 • Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang
Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples.
no code implementations • 2 Mar 2023 • Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang
To circumvent this difficulty, we examine the problem of identifying a mixed Nash equilibrium, where strategies are randomized and characterized by probability distributions over continuous domains. To this end, we propose PArticle-based Primal-dual ALgorithm (PAPAL) tailored for a weakly entropy-regularized min-max optimization over probability distributions.
1 code implementation • 3 Jan 2023 • Yanwei Fu, Xiaomei Wang, Hanze Dong, Yu-Gang Jiang, Meng Wang, xiangyang xue, Leonid Sigal
Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels.
no code implementations • 25 Nov 2022 • Hanze Dong, Xi Wang, Yong Lin, Tong Zhang
With the popularity of Stein variational gradient descent (SVGD), the focus of particle-based VI algorithms has been on the properties of functions in Reproducing Kernel Hilbert Space (RKHS) to approximate the gradient flow.
1 code implementation • 21 Nov 2022 • Hanze Dong, Shizhe Diao, Weizhong Zhang, Tong Zhang
The resulting method is significantly more powerful than the standard normalization flow approach for generating data distributions with multiple modes.
no code implementations • 29 Sep 2022 • Songtao Liu, Rex Ying, Hanze Dong, Lu Lin, Jinghui Chen, Dinghao Wu
However, the analysis of implicit denoising effect in graph neural networks remains open.
no code implementations • CVPR 2022 • Yong Lin, Hanze Dong, Hao Wang, Tong Zhang
Generalization under distributional shift is an open challenge for machine learning.
1 code implementation • 8 Sep 2021 • Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu
To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.
1 code implementation • 27 Dec 2020 • Cong Fang, Hanze Dong, Tong Zhang
Deep learning has received considerable empirical successes in recent years.
1 code implementation • 6 Oct 2020 • Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information.
no code implementations • 11 Nov 2019 • Songtao Liu, Lingwei Chen, Hanze Dong, ZiHao Wang, Dinghao Wu, Zengfeng Huang
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.
no code implementations • 25 Oct 2019 • Cong Fang, Hanze Dong, Tong Zhang
Recently, over-parameterized neural networks have been extensively analyzed in the literature.
no code implementations • ICLR 2019 • Hanze Dong, Yanwei Fu, Sung Ju Hwang, Leonid Sigal, xiangyang xue
This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time.
no code implementations • 28 May 2017 • Yanwei Fu, Hanze Dong, Yu-feng Ma, Zhengjun Zhang, xiangyang xue
To solve this problem, we propose the Extreme Value Learning (EVL) formulation to learn the mapping from visual feature to semantic space.