Jiaming Song
60 papers · 2016–2026 · 11 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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(43)
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(7)
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Research topics
Deep Learning
(41)
Machine Learning
(41)
Models
(31)
Artificial Intelligence
(20)
Learning Types Β· Machine Learning
(20)
Optimization & Theory
(17)
Computer Vision
(14)
Core AI
(14)
Learning Types Β· Deep Learning
(13)
Reinforcement Learning
(11)
Core Methods
(11)
Generation
(10)
Methods
(10)
Application Areas
(8)
Learning Paradigms
(6)
Keywords
diffusion model
(14)
generative model
(9)
image generation
(7)
representation learning
(6)
variational inference
(6)
imitation learning
(6)
density estimation
(5)
generative adversarial network
(4)
importance sampling
(4)
neural network
(3)
contrastive learning
(3)
image restoration
(3)
score matching
(3)
mutual information
(3)
inverse reinforcement learning
(3)
density ratio estimation
(3)
adversarial learning
(2)
preference optimization
(2)
likelihood-free inference
(2)
reinforcement learning
(2)
Papers
SABER: Switchable and Balanced Training for Efficient LLM Reasoning
AAAI 2026
Self-NPO: Data-Free Diffusion Model Enhancement via Truncated Diffusion Fine-Tuning
AAAI 2026
Decentralized Diffusion Models
CVPR 2025
Inductive Moment Matching
ICML 2025
Personalized Preference Fine-tuning of Diffusion Models
CVPR 2025
Score-Based Diffusion Models in Function Space
JMLR 2025
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
AAAI 2024
A Variational Perspective on Solving Inverse Problems with Diffusion Models
ICLR 2024
DiffiT: Diffusion Vision Transformers for Image Generation
ECCV 2024
Seer: Language Instructed Video Prediction with Latent Diffusion Models
ICLR 2024
Affordance Diffusion: Synthesizing Hand-Object Interactions
CVPR 2023
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
ICML 2023
DiffCollage: Parallel Generation of Large Content With Diffusion Models
CVPR 2023
Dual Diffusion Implicit Bridges for Image-to-Image Translation
ICLR 2023
Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation
ICML 2023
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching
AAAI 2023
PhysDiff: Physics-Guided Human Motion Diffusion Model
ICCV 2023
Pseudoinverse-Guided Diffusion Models for Inverse Problems
ICLR 2023
A General Recipe for Likelihood-free Bayesian Optimization
ICML 2022
BigDL 2.0: Seamless Scaling of AI Pipelines From Laptops to Distributed Cluster
CVPR 2022
Comparing Distributions by Measuring Differences that Affect Decision Making
ICLR 2022
Concrete Score Matching: Generalized Score Matching for Discrete Data
NeurIPS 2022
Denoising Diffusion Restoration Models
NeurIPS 2022
Experience Replay with Likelihood-free Importance Weights
L4DC 2022
IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling
AAAI 2022
LISA: Learning Interpretable Skill Abstractions from Language
NeurIPS 2022
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
ICLR 2022
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
NeurIPS 2021
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation
NeurIPS 2021
Denoising Diffusion Implicit Models
ICLR 2021
Imitation with Neural Density Models
NeurIPS 2021
Improved Autoregressive Modeling with Distribution Smoothing
ICLR 2021
IQ-Learn: Inverse soft-Q Learning for Imitation
NeurIPS 2021
Negative Data Augmentation
ICLR 2021
Pseudo-Spherical Contrastive Divergence
NeurIPS 2021
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
NeurIPS 2021
A Theory of Usable Information under Computational Constraints
ICLR 2020
Autoregressive Score Matching
NeurIPS 2020
Belief Propagation Neural Networks
NeurIPS 2020
Bridging the Gap Between f-GANs and Wasserstein GANs
ICML 2020
Domain Adaptive Imitation Learning
ICML 2020
Gaussianization Flows
AISTATS 2020
Multi-label Contrastive Predictive Coding
NeurIPS 2020
Permutation Invariant Graph Generation via Score-Based Generative Modeling
AISTATS 2020
Robust and On-the-fly Dataset Denoising for Image Classification
ECCV 2020
Training Deep Energy-Based Models with f-Divergence Minimization
ICML 2020
Understanding the Limitations of Variational Mutual Information Estimators
ICLR 2020
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
NeurIPS 2019
Calibrated Model-Based Deep Reinforcement Learning
ICML 2019
InfoVAE: Balancing Learning and Inference in Variational Autoencoders
AAAI 2019
Learning Controllable Fair Representations
AISTATS 2019
Multi-Agent Adversarial Inverse Reinforcement Learning
ICML 2019
Accelerating Natural Gradient with Higher-Order Invariance
ICML 2018
Adversarial Constraint Learning for Structured Prediction
IJCAI 2018
Bias and Generalization in Deep Generative Models: An Empirical Study
NeurIPS 2018
Multi-Agent Generative Adversarial Imitation Learning
NeurIPS 2018
A-NICE-MC: Adversarial Training for MCMC
NeurIPS 2017
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
NeurIPS 2017
Learning Hierarchical Features from Deep Generative Models
ICML 2017
Factored Temporal Sigmoid Belief Networks for Sequence Learning
ICML 2016