Image de couverture de livre

Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhu, Yaxuan, Xie, Jianwen, Wu, Yingnian, Gao, Ruiqi
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation
par: Song, Weinan, et autres
Publié: (2023)

Optimizing the Unknown: Black Box Bayesian Optimization with Energy-Based Model and Reinforcement Learning
par: Miao, Ruiyao, et autres
Publié: (2025)

CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing
par: Doan, Khoa D., et autres
Publié: (2022)

"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood
par: Yu, Peiyu, et autres
Publié: (2024)

Learning Energy-Based Models by Self-normalising the Likelihood
par: Senetaire, Hugo, et autres
Publié: (2025)

Think Twice Before You Act: Improving Inverse Problem Solving With MCMC
par: Zhu, Yaxuan, et autres
Publié: (2024)

Relational Knowledge Distillation Using Fine-tuned Function Vectors
par: Kang, Andrea, et autres
Publié: (2026)

Likelihood Matching for Diffusion Models
par: Qian, Lei, et autres
Publié: (2025)

Stabilizing Self-Consuming Diffusion Models with Latent Space Filtering
par: Cai, Zhongteng, et autres
Publié: (2025)

Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
par: Zheng, Kaiwen, et autres
Publié: (2023)

Latent Space Energy-based Neural ODEs
par: Cheng, Sheng, et autres
Publié: (2024)

Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
par: Sharrock, Louis, et autres
Publié: (2022)

Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation
par: Chang, Yingshan, et autres
Publié: (2024)

Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
par: Choi, Jaemoo, et autres
Publié: (2026)

Diffusion Model Based Signal Recovery Under 1-Bit Quantization
par: Chen, Youming, et autres
Publié: (2025)

Likelihood-guided Regularization in Attention Based Models
par: Salem, Mohamed, et autres
Publié: (2025)

VFScale: Intrinsic Reasoning through Verifier-Free Test-time Scalable Diffusion Model
par: Zhang, Tao, et autres
Publié: (2025)

EM Distillation for One-step Diffusion Models
par: Xie, Sirui, et autres
Publié: (2024)

Lookahead Path Likelihood Optimization for Diffusion LLMs
par: Liu, Xuejie, et autres
Publié: (2026)

Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
par: Zhu, Linglingzhi, et autres
Publié: (2025)

LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models
par: Wei, Chenxing, et autres
Publié: (2026)

Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learning
par: Wang, Ziyan, et autres
Publié: (2024)

Maximum Likelihood Reinforcement Learning
par: Tajwar, Fahim, et autres
Publié: (2026)

Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks
par: Alipourfard, Borzou, et autres
Publié: (2018)

Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models
par: Wang, Yule, et autres
Publié: (2023)

d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation
par: Wang, Guanghan, et autres
Publié: (2025)

Hardness of Maximum Likelihood Learning of DPPs
par: Grigorescu, Elena, et autres
Publié: (2022)

Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models
par: Yoon, Sangwoong, et autres
Publié: (2024)

Latent Diffusion-Based 3D Molecular Recovery from Vibrational Spectra
par: Wu, Wenjin, et autres
Publié: (2026)

DUEL: Exact Likelihood for Masked Diffusion via Deterministic Unmasking
par: Turok, Gilad, et autres
Publié: (2026)

Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models
par: Erives, Ezra, et autres
Publié: (2024)

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
par: Zhang, Ruiqi, et autres
Publié: (2025)

Multi-AUV Cooperative Target Tracking Based on Supervised Diffusion-Aided Multi-Agent Reinforcement Learning
par: Ma, Jiaao, et autres
Publié: (2026)

Molecule Design by Latent Prompt Transformer
par: Kong, Deqian, et autres
Publié: (2023)

Fixed Random Classifier Rearrangement for Continual Learning
par: Huang, Shengyang, et autres
Publié: (2024)

Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps
par: Li, Henry, et autres
Publié: (2025)

All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning
par: Swamy, Gokul, et autres
Publié: (2025)

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning is A Context Modeling Problem
par: Li, Chao, et autres
Publié: (2025)

Mixed Likelihood Variational Gaussian Processes
par: Wu, Kaiwen, et autres
Publié: (2025)

FFT-Accelerated Auxiliary Variable MCMC for Fermionic Lattice Models: A Determinant-Free Approach with $O(N\log N)$ Complexity
par: Kong, Deqian, et autres
Publié: (2025)