1.3k citations · 3.6k across the 28 of their papers we have counts for
5 papers · 1 filter
Unsupervised Out-of-Distribution Detection with Batch Normalization
Jiaming Song, Yang Song, Stefano Ermon
Likelihood from a generative model is a natural statistic for detecting out-of-distribution (OoD) samples. However, generative models have been shown to assign higher likelihood to…
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song +6
We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nod…
MintNet: Building Invertible Neural Networks with Masked Convolutions
Yang Song, Chenlin Meng, Stefano Ermon
We propose a new way of constructing invertible neural networks by combining simple building blocks with a novel set of composition rules. This leads to a rich set of invertible ar…
Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song, Stefano Ermon
We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can b…
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Yang Song, Sahaj Garg, Jiaxin Shi +1
Score matching is a popular method for estimating unnormalized statistical models. However, it has been so far limited to simple, shallow models or low-dimensional data, due to the…