1.3k citations · 3.6k across the 28 of their papers we have counts for
11 papers · 1 filter
Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole +2
While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on di…
Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma +3
Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distr…
Autoregressive Score Matching
Chenlin Meng, Lantao Yu, Yang Song +2
Autoregressive models use chain rule to define a joint probability distribution as a product of conditionals. These conditionals need to be normalized, imposing constraints on the…
Imitation with Neural Density Models
Kuno Kim, Akshat Jindal, Yang Song +3
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) us…
Understanding Classifier Mistakes with Generative Models
Laëtitia Shao, Yang Song, Stefano Ermon
Although deep neural networks are effective on supervised learning tasks, they have been shown to be brittle. They are prone to overfitting on their training distribution and are e…
Efficient Learning of Generative Models via Finite-Difference Score Matching
Tianyu Pang, Kun Xu, Chongxuan Li +3
Several machine learning applications involve the optimization of higher-order derivatives (e.g., gradients of gradients) during training, which can be expensive in respect to memo…
Improved Techniques for Training Score-Based Generative Models
Yang Song, Stefano Ermon
Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization. However, existing training procedures are limit…
Gaussianization Flows
Chenlin Meng, Yang Song, Jiaming Song +1
Iterative Gaussianization is a fixed-point iteration procedure that can transform any continuous random vector into a Gaussian one. Based on iterative Gaussianization, we propose a…
Permutation Invariant Graph Generation via Score-Based Generative Modeling
Chenhao Niu, Yang Song, Jiaming Song +3
Learning generative models for graph-structured data is challenging because graphs are discrete, combinatorial, and the underlying data distribution is invariant to the ordering of…
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks
Yusuke Tashiro, Yang Song, Stefano Ermon
Adversarial attacks often involve random perturbations of the inputs drawn from uniform or Gaussian distributions, e.g., to initialize optimization-based white-box attacks or gener…
Training Deep Energy-Based Models with f-Divergence Minimization
Lantao Yu, Yang Song, Jiaming Song +1
Deep energy-based models (EBMs) are very flexible in distribution parametrization but computationally challenging because of the intractable partition function. They are typically…