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Featured Publications
An Energy-Based Prior for Generative Saliency
Authors: Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
In this paper, we introduce a novel energy-based prior for generative saliency models. Our approach enhances the interpretability and performance of saliency detection by integrating energy-based techniques, leading to more accurate and reliable results in various applications.
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Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
In this paper, we introduce a novel energy-based prior for generative saliency models. Our approach enhances the interpretability and performance of saliency detection by integrating energy-based techniques, leading to more accurate and reliable results in various applications.
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Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood
Authors: Yaxuan Zhu, Jianwen Xie, Ying Nian Wu, Ruiqi Gao
Conference: The Twelfth International Conference on Learning Representations (ICLR), 2024
This research presents an innovative method for learning energy-based models using cooperative diffusion recovery likelihood. Our approach leverages the strengths of cooperative learning and diffusion processes to improve the training efficiency and effectiveness of energy-based models.
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Conference: The Twelfth International Conference on Learning Representations (ICLR), 2024
This research presents an innovative method for learning energy-based models using cooperative diffusion recovery likelihood. Our approach leverages the strengths of cooperative learning and diffusion processes to improve the training efficiency and effectiveness of energy-based models.
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Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation
Authors: Weinan Song, Yaxuan Zhu, Lei He, Ying Nian Wu, Jianwen Xie
Archive: ArXiv, 2024
We propose a progressive energy-based cooperative learning framework for multi-domain image-to-image translation. This method addresses the challenges of domain adaptation and translation by progressively refining the learning process, resulting in superior performance across multiple domains.
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Archive: ArXiv, 2024
We propose a progressive energy-based cooperative learning framework for multi-domain image-to-image translation. This method addresses the challenges of domain adaptation and translation by progressively refining the learning process, resulting in superior performance across multiple domains.
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Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference
Authors: Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian Wu
In this paper, we introduce the Latent Plan Transformer, a novel framework that treats planning as latent variable inference. Our approach combines the strengths of transformers and latent variable models to achieve robust and efficient planning in complex environments.
Conference: The 38th Conference on Neural Information Processing Systems (NeurIPS 2024)
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In this paper, we introduce the Latent Plan Transformer, a novel framework that treats planning as latent variable inference. Our approach combines the strengths of transformers and latent variable models to achieve robust and efficient planning in complex environments.
Conference: The 38th Conference on Neural Information Processing Systems (NeurIPS 2024)
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CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing
Authors: Khoa Doan, Jianwen Xie, Yaxuan Zhu, Yang Zhao, Ping Li
CoopHash introduces a novel approach to supervised image hashing through cooperative learning. By leveraging Variational Markov Chain Monte Carlo (MCMC) teaching, it simultaneously optimizes a multipurpose descriptor and a contrastive pair generator. This innovative method enhances the efficiency and accuracy of image hashing, making it a significant advancement in the field.
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CoopHash introduces a novel approach to supervised image hashing through cooperative learning. By leveraging Variational Markov Chain Monte Carlo (MCMC) teaching, it simultaneously optimizes a multipurpose descriptor and a contrastive pair generator. This innovative method enhances the efficiency and accuracy of image hashing, making it a significant advancement in the field.
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Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space
Authors: Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu
This paper introduces a novel black-box optimization method known as the Latent Energy-Based Odyssey. The approach focuses on expanded exploration within an energy-based latent space, enhancing the optimization process. By leveraging the latent space’s energy landscape, the method improves the efficiency and effectiveness of optimization tasks, making it a significant advancement in the field of machine learning and optimization.
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This paper introduces a novel black-box optimization method known as the Latent Energy-Based Odyssey. The approach focuses on expanded exploration within an energy-based latent space, enhancing the optimization process. By leveraging the latent space’s energy landscape, the method improves the efficiency and effectiveness of optimization tasks, making it a significant advancement in the field of machine learning and optimization.
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Molecule Design by Latent Prompt Transformer
Authors: Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu
We propose the Latent Prompt Transformer (LPT), a novel generative model for challenging problem of molecule design. Experiments demonstrate that LPT not only effectively discovers useful molecules across single-objective, multi-objective, and structure-constrained optimization tasks, but also exhibits strong sample efficiency.
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We propose the Latent Prompt Transformer (LPT), a novel generative model for challenging problem of molecule design. Experiments demonstrate that LPT not only effectively discovers useful molecules across single-objective, multi-objective, and structure-constrained optimization tasks, but also exhibits strong sample efficiency.
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