Alexander A. Alemi

According to our database, Alexander A. Alemi authored at least 40 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

CoRR, 2023

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?

C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein

CoRR, 2023

Small-scale proxies for large-scale Transformer training instabilities.

Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith

CoRR, 2023

Speed Limits for Deep Learning.

Inbar Seroussi, Alexander A. Alemi, Moritz Helias, Zohar Ringel

CoRR, 2023

Variational Prediction.

Alexander A. Alemi, Ben Poole

CoRR, 2023

Weighted Ensemble Self-Supervised Learning.

Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Bayesian Imitation Learning for End-to-End Mobile Manipulation.

Yuqing Du, Daniel Ho, Alex Alemi, Eric Jang, Mohi Khansari

Proceedings of the International Conference on Machine Learning, 2022

PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime.

Warren R. Morningstar, Alex Alemi, Joshua V. Dillon

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

A Closer Look at the Adversarial Robustness of Information Bottleneck Models.

Iryna Korshunova, David Stutz, Alexander A. Alemi, Olivia Wiles, Sven Gowal

CoRR, 2021

Does Knowledge Distillation Really Work?

Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Density of States Estimation for Out of Distribution Detection.

Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

CEB Improves Model Robustness.

Ian Fischer, Alexander A. Alemi

Entropy, 2020

VIB is Half Bayes.

Alexander A. Alemi, Warren R. Morningstar, Ben Poole, Ian Fischer, Joshua V. Dillon

CoRR, 2020

PAC^m-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime.

Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon

CoRR, 2020

Neural Tangents: Fast and Easy Infinite Neural Networks in Python.

Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Thermodynamic Computing.

Tom Conte, Erik DeBenedictis, Natesh Ganesh, Todd Hylton, John Paul Strachan, R. Stanley Williams, Alexander A. Alemi, Lee Altenberg, Gavin E. Crooks, James P. Crutchfield, Lídia del Rio, Josh Deutsch, Michael Robert DeWeese, Khari Douglas, Massimiliano Esposito, Michael P. Frank, Robert Fry, Peter Harsha, Mark D. Hill, Christopher T. Kello, Jeff Krichmar, Suhas Kumar, Shih-Chii Liu, Seth Lloyd, Matteo Marsili, Ilya Nemenman, Alex Nugent, Norman H. Packard, Dana Randall, Peter Sadowski, Narayana Santhanam, Robert Shaw, Adam Z. Stieg, Elan Stopnitzky, Christof Teuscher, Chris Watkins, David H. Wolpert, J. Joshua Yang, Yan Yufik

CoRR, 2019

On Predictive Information Sub-optimality of RNNs.

Zhe Dong, Deniz Oktay, Ben Poole, Alexander A. Alemi

CoRR, 2019

Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces.

Bryan Seybold, Emily Fertig, Alex Alemi, Ian Fischer

CoRR, 2019

On the Use of ArXiv as a Dataset.

Colin B. Clement, Matthew Bierbaum, Kevin P. O'Keeffe, Alexander A. Alemi

CoRR, 2019

On Variational Bounds of Mutual Information.

Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker

Proceedings of the 36th International Conference on Machine Learning, 2019

Information in Infinite Ensembles of Infinitely-Wide Neural Networks.

Ravid Shwartz-Ziv, Alexander A. Alemi

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

Variational Predictive Information Bottleneck.

Alexander A. Alemi

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018

β-VAEs can retain label information even at high compression.

Emily Fertig, Aryan Arbabi, Alexander A. Alemi

CoRR, 2018

TherML: Thermodynamics of Machine Learning.

Alexander A. Alemi, Ian Fischer

CoRR, 2018

Uncertainty in the Variational Information Bottleneck.

Alexander A. Alemi, Ian Fischer, Joshua V. Dillon

CoRR, 2018

GILBO: One Metric to Measure Them All.

Alexander A. Alemi, Ian Fischer

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Watch Your Step: Learning Node Embeddings via Graph Attention.

Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Fixing a Broken ELBO.

Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

TensorFlow Distributions.

Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous

CoRR, 2017

An Information-Theoretic Analysis of Deep Latent-Variable Models.

Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy

CoRR, 2017

Watch Your Step: Learning Graph Embeddings Through Attention.

Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi

CoRR, 2017

Jeffrey's prior sampling of deep sigmoidal networks.

Lorien X. Hayden, Alexander A. Alemi, Paul H. Ginsparg, James P. Sethna

CoRR, 2017

Motion Prediction Under Multimodality with Conditional Stochastic Networks.

Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco, Rahul Sukthankar

CoRR, 2017

Deep Variational Information Bottleneck.

Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy

Proceedings of the 5th International Conference on Learning Representations, 2017

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.

Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander A. Alemi

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Improved generator objectives for GANs.

Ben Poole, Alexander A. Alemi, Jascha Sohl-Dickstein, Anelia Angelova

CoRR, 2016

DeepMath - Deep Sequence Models for Premise Selection.

Alexander A. Alemi, François Chollet, Geoffrey Irving, Christian Szegedy, Josef Urban

CoRR, 2016

DeepMath - Deep Sequence Models for Premise Selection.

Geoffrey Irving, Christian Szegedy, Alexander A. Alemi, Niklas Eén, François Chollet, Josef Urban

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Clustering via Content-Augmented Stochastic Blockmodels.

J. Massey Cashore, Xiaoting Zhao, Alexander A. Alemi, Yujia Liu, Peter I. Frazier

CoRR, 2015

Text Segmentation based on Semantic Word Embeddings.

Alexander A. Alemi, Paul Ginsparg

CoRR, 2015