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In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Despite recent empirical success, the inductive bias of deep generative models is not well understood. In this paper we propose a framework to systematically investigate bias and generalization in deep generative models of images by probing the learning algorithm with carefully designed training datasets. By measuring properties of the learned distribution, we are able to find interesting patterns of generalization. We verify that these patterns are consistent across datasets, common models and architectures.
Author Information
Shengjia Zhao (Stanford University)
Hongyu Ren (Stanford University)
Arianna Yuan (Stanford University)
Jiaming Song (Stanford University)
I am a first year Ph.D. student in Stanford University. I think about problems in machine learning and deep learning under the supervision of Stefano Ermon. I did my undergrad at Tsinghua University, where I was lucky enough to collaborate with Jun Zhu and Lawrence Carin on scalable Bayesian machine learning.
Noah Goodman (Stanford University)
Stefano Ermon (Stanford)
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2018 Poster: Multimodal Generative Models for Scalable Weakly-Supervised Learning »
Mike Wu · Noah Goodman -
2017 : Generative Adversarial Imitation Learning, Stefano Ermon, Stanford »
Stefano Ermon -
2017 : Morning panel discussion »
Jürgen Schmidhuber · Noah Goodman · Anca Dragan · Pushmeet Kohli · Dhruv Batra -
2017 : "Language in context" »
Noah Goodman -
2017 : Stefano Ermon (Stanford): Measuring Progress Towards Sustainable Development Goals with Machine Learning »
Stefano Ermon -
2017 Poster: A-NICE-MC: Adversarial Training for MCMC »
Jiaming Song · Shengjia Zhao · Stefano Ermon -
2017 Poster: InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations »
Yunzhu Li · Jiaming Song · Stefano Ermon -
2017 Poster: Learning Disentangled Representations with Semi-Supervised Deep Generative Models »
Siddharth Narayanaswamy · Brooks Paige · Jan-Willem van de Meent · Alban Desmaison · Noah Goodman · Pushmeet Kohli · Frank Wood · Philip Torr -
2017 Poster: Neural Variational Inference and Learning in Undirected Graphical Models »
Volodymyr Kuleshov · Stefano Ermon -
2016 Poster: Solving Marginal MAP Problems with NP Oracles and Parity Constraints »
Yexiang Xue · zhiyuan li · Stefano Ermon · Carla Gomes · Bart Selman -
2016 Poster: Generative Adversarial Imitation Learning »
Jonathan Ho · Stefano Ermon -
2016 Poster: Variational Bayes on Monte Carlo Steroids »
Aditya Grover · Stefano Ermon -
2016 Poster: Adaptive Concentration Inequalities for Sequential Decision Problems »
Shengjia Zhao · Enze Zhou · Ashish Sabharwal · Stefano Ermon -
2016 Poster: Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks »
Daniel Ritchie · Anna Thomas · Pat Hanrahan · Noah Goodman -
2015 Workshop: Bounded Optimality and Rational Metareasoning »
Samuel J Gershman · Falk Lieder · Tom Griffiths · Noah Goodman -
2013 Poster: Embed and Project: Discrete Sampling with Universal Hashing »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2013 Poster: Learning and using language via recursive pragmatic reasoning about other agents »
Nathaniel J Smith · Noah Goodman · Michael C Frank -
2013 Poster: Learning Stochastic Inverses »
Andreas Stuhlmüller · Jacob Taylor · Noah Goodman -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Poster: Density Propagation and Improved Bounds on the Partition Function »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2012 Poster: Burn-in, bias, and the rationality of anchoring »
Falk Lieder · Tom Griffiths · Noah Goodman -
2011 Poster: Accelerated Adaptive Markov Chain for Partition Function Computation »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2011 Spotlight: Accelerated Adaptive Markov Chain for Partition Function Computation »
Stefano Ermon · Carla Gomes · Ashish Sabharwal · Bart Selman -
2011 Poster: Nonstandard Interpretations of Probabilistic Programs for Efficient Inference »
David Wingate · Noah Goodman · Andreas Stuhlmueller · Jeffrey Siskind