Search Results for author: Hanze Dong

Found 15 papers, 6 papers with code

DetGPT: Detect What You Need via Reasoning

no code implementations23 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.

Autonomous Driving object-detection +1

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

1 code implementation13 Apr 2023 Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang

Prior research has primarily employed Reinforcement Learning from Human Feedback (RLHF) as a means of addressing this problem, wherein generative models are fine-tuned using RL algorithms guided by a human-feedback-informed reward model.


Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium

no code implementations2 Mar 2023 Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang

We consider the general nonconvex nonconcave minimax problem over continuous variables.

Vocabulary-informed Zero-shot and Open-set Learning

1 code implementation3 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.

Object Categorization Open Set Learning +1

Particle-based Variational Inference with Preconditioned Functional Gradient Flow

no code implementations25 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.

Variational Inference

Normalizing Flow with Variational Latent Representation

1 code implementation21 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.

Bayesian Invariant Risk Minimization

no code implementations CVPR 2022 Yong Lin, Hanze Dong, Hao Wang, Tong Zhang

Generalization under distributional shift is an open challenge for machine learning.

Bayesian Inference

Local Augmentation for Graph Neural Networks

1 code implementation8 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.

Mathematical Models of Overparameterized Neural Networks

1 code implementation27 Dec 2020 Cong Fang, Hanze Dong, Tong Zhang

Deep learning has received considerable empirical successes in recent years.

Weakly Supervised Disentangled Generative Causal Representation Learning

1 code implementation6 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.


Higher-order Weighted Graph Convolutional Networks

no code implementations11 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.

Node Classification

Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations

no code implementations25 Oct 2019 Cong Fang, Hanze Dong, Tong Zhang

Recently, over-parameterized neural networks have been extensively analyzed in the literature.

Learning the Compositional Spaces for Generalized Zero-shot Learning

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.

Generalized Zero-Shot Learning Open Set Learning

Vocabulary-informed Extreme Value Learning

no code implementations28 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.

Open Set Learning

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