Search Results for author: Zhendong Wang

Found 32 papers, 19 papers with code

Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization

1 code implementation22 Feb 2024 Xuxi Chen, Zhendong Wang, Daouda Sow, Junjie Yang, Tianlong Chen, Yingbin Liang, Mingyuan Zhou, Zhangyang Wang

Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets, with a specific focus on selective retention of samples that incur moderately high losses.

Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts

no code implementations12 Feb 2024 Yueqin Yin, Zhendong Wang, Yi Gu, Hai Huang, Weizhu Chen, Mingyuan Zhou

In the field of large language models (LLMs), aligning models with the diverse preferences of users is a critical challenge.

Anything in Any Scene: Photorealistic Video Object Insertion

no code implementations30 Jan 2024 Chen Bai, Zeman Shao, Guoxiang Zhang, Di Liang, Jie Yang, Zhuorui Zhang, Yujian Guo, Chengzhang Zhong, Yiqiao Qiu, Zhendong Wang, Yichen Guan, Xiaoyin Zheng, Tao Wang, Cheng Lu

Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism.

Data Augmentation Object +2

Improving In-Context Learning in Diffusion Models with Visual Context-Modulated Prompts

no code implementations3 Dec 2023 Tianqi Chen, Yongfei Liu, Zhendong Wang, Jianbo Yuan, Quanzeng You, Hongxia Yang, Mingyuan Zhou

In light of the remarkable success of in-context learning in large language models, its potential extension to the vision domain, particularly with visual foundation models like Stable Diffusion, has sparked considerable interest.

In-Context Learning

Counterfactual Explanations for Time Series Forecasting

1 code implementation12 Oct 2023 Zhendong Wang, Ioanna Miliou, Isak Samsten, Panagiotis Papapetrou

In this paper, we formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF, that solves the problem by applying gradient-based perturbations to the original time series.

counterfactual Time Series +1

Beta Diffusion

1 code implementation NeurIPS 2023 Mingyuan Zhou, Tianqi Chen, Zhendong Wang, Huangjie Zheng

We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges.

Denoising

AltFreezing for More General Video Face Forgery Detection

1 code implementation CVPR 2023 Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Houqiang Li

In this paper, we propose to capture both spatial and temporal artifacts in one model for face forgery detection.

Data Augmentation

MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting

no code implementations12 Jun 2023 Xing Wang, Zhendong Wang, Kexin Yang, Junlan Feng, Zhiyan Song, Chao Deng, Lin Zhu

To capture the intrinsic patterns of time series, we propose a novel deep learning network architecture, named Multi-resolution Periodic Pattern Network (MPPN), for long-term series forecasting.

Time Series Time Series Forecasting

Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models

1 code implementation NeurIPS 2023 Zhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang, Pengcheng He, Zhangyang Wang, Weizhu Chen, Mingyuan Zhou

Patch Diffusion meanwhile improves the performance of diffusion models trained on relatively small datasets, $e. g.$, as few as 5, 000 images to train from scratch.

SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks

no code implementations12 Apr 2023 Haojia Yu, Han Hu, Bo Xu, Qisen Shang, Zhendong Wang, Qing Zhu

Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images.

Segmentation Semantic Segmentation +2

DIRE for Diffusion-Generated Image Detection

1 code implementation ICCV 2023 Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, Houqiang Li

We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data.

Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients

1 code implementation Artificial Intelligence in Medicine 2023 Zhendong Wang, Isak Samsten, Vasiliki Kougia, Panagiotis Papapetrou

In this paper, we propose a counterfactual solution MedSeqCF for preventing the mortality of three cohorts of ICU patients, by representing their electronic health records as medical event sequences, and generating counterfactuals by adopting and employing a text style-transfer technique.

counterfactual Counterfactual Explanation +3

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

2 code implementations12 Aug 2022 Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou

In our approach, we learn an action-value function and we add a term maximizing action-values into the training loss of the conditional diffusion model, which results in a loss that seeks optimal actions that are near the behavior policy.

D4RL Offline RL +3

Probabilistic Conformal Prediction Using Conditional Random Samples

1 code implementation14 Jun 2022 Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei

This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set.

Conformal Prediction Prediction Intervals

Network Topology Optimization via Deep Reinforcement Learning

no code implementations19 Apr 2022 Zhuoran Li, Xing Wang, Ling Pan, Lin Zhu, Zhendong Wang, Junlan Feng, Chao Deng, Longbo Huang

A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.

Management reinforcement-learning +1

Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving

no code implementations22 Feb 2022 Xiaoming Zeng, Zhendong Wang, Yang Hu

We also propose a Layer-sharing technique in the deep layer that can achieve better accuracy with less computational overhead.

Autonomous Driving Decision Making +1

A Behavior Regularized Implicit Policy for Offline Reinforcement Learning

no code implementations19 Feb 2022 Shentao Yang, Zhendong Wang, Huangjie Zheng, Yihao Feng, Mingyuan Zhou

For training more effective agents, we propose a framework that supports learning a flexible yet well-regularized fully-implicit policy.

D4RL reinforcement-learning +1

Learning Time Series Counterfactuals via Latent Space Representations

1 code implementation International Conference on Discovery Science 2021 Zhendong Wang, Isak Samsten, Rami Mochaourab, Panagiotis Papapetrou

Counterfactual explanations can provide sample-based explanations of features required to modify from the original sample to change the classification result from an undesired state to a desired state; hence it provides interpretability of the model.

counterfactual Counterfactual Explanation +3

State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning

no code implementations29 Sep 2021 Shentao Yang, Zhendong Wang, Huangjie Zheng, Mingyuan Zhou

For training more effective agents, we propose a framework that supports learning a flexible and well-regularized policy, which consists of a fully implicit policy and a regularization through the state-action visitation frequency induced by the current policy and that induced by the data-collecting behavior policy.

D4RL reinforcement-learning +1

Implicit Distributional Reinforcement Learning

3 code implementations NeurIPS 2020 Yuguang Yue, Zhendong Wang, Mingyuan Zhou

To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution.

Distributional Reinforcement Learning OpenAI Gym +2

Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation

1 code implementation ICLR 2020 Xinjie Fan, Yizhe Zhang, Zhendong Wang, Mingyuan Zhou

To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.

Image Captioning Program Synthesis

Thompson Sampling via Local Uncertainty

1 code implementation ICML 2020 Zhendong Wang, Mingyuan Zhou

Variational inference is used to approximate the posterior of the local variable, and semi-implicit structure is further introduced to enhance its expressiveness.

Decision Making Multi-Armed Bandits +2

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