Jascha Sohl-Dickstein

According to our database1, Jascha Sohl-Dickstein authored at least 33 papers between 2011 and 2018.

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Bibliography

2018
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

PCA of high dimensional random walks with comparison to neural network training.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks.
Proceedings of the 35th International Conference on Machine Learning, 2018

Sensitivity and Generalization in Neural Networks: an Empirical Study.
Proceedings of the 6th International Conference on Learning Representations, 2018

Learning to Learn Without Labels.
Proceedings of the 6th International Conference on Learning Representations, 2018

Generalizing Hamiltonian Monte Carlo with Neural Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Deep Neural Networks as Gaussian Processes.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Minimum and Maximum Entropy Distributions for Binary Systems with Known Means and Pairwise Correlations.
Entropy, 2017

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learned Optimizers that Scale and Generalize.
Proceedings of the 34th International Conference on Machine Learning, 2017

On the Expressive Power of Deep Neural Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability.
Proceedings of the 34th International Conference on Machine Learning, 2017

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.
Proceedings of the 5th International Conference on Learning Representations, 2017

Deep Information Propagation.
Proceedings of the 5th International Conference on Learning Representations, 2017

Unrolled Generative Adversarial Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017

Explaining the Learning Dynamics of Direct Feedback Alignment.
Proceedings of the 5th International Conference on Learning Representations, 2017

Density estimation using Real NVP.
Proceedings of the 5th International Conference on Learning Representations, 2017

Capacity and Trainability in Recurrent Neural Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Exponential expressivity in deep neural networks through transient chaos.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
A Device for Human Ultrasonic Echolocation.
IEEE Trans. Biomed. Engineering, 2015

Deep Knowledge Tracing.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Deep Unsupervised Learning using Nonequilibrium Thermodynamics.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Modeling Higher-Order Correlations within Cortical Microcolumns.
PLoS Computational Biology, 2014

Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods.
Proceedings of the 31th International Conference on Machine Learning, 2014

Hamiltonian Monte Carlo Without Detailed Balance.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Measurably Increasing Motivation in MOOCs.
Proceedings of the Workshops at the 16th International Conference on Artificial Intelligence in Education AIED 2013, 2013

Controlled experiments on millions of students to personalize learning.
Proceedings of the Workshops at the 16th International Conference on Artificial Intelligence in Education AIED 2013, 2013

2012
Efficient Methods for Unsupervised Learning of Probabilistic Models.
PhD thesis, 2012

Training sparse natural image models with a fast Gibbs sampler of an extended state space.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
Minimum Probability Flow Learning.
Proceedings of the 28th International Conference on Machine Learning, 2011

Building a better probabilistic model of images by factorization.
Proceedings of the IEEE International Conference on Computer Vision, 2011

Lie Group Transformation Models for Predictive Video Coding.
Proceedings of the 2011 Data Compression Conference (DCC 2011), 2011


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