Text Segmentation based on Semantic Word Embeddings. - Alexander A. Alemi; Paul Ginsparg (CoRR, 2015)

Clustering via Content-Augmented Stochastic Blockmodels. - J. Massey Cashore; Xiaoting Zhao; Alexander A. Alemi; Yujia Liu; Peter I. Frazier (CoRR, 2015)

DeepMath - Deep Sequence Models for Premise Selection. - Geoffrey Irving; Christian Szegedy; Alexander A. Alemi; Niklas Eén; François Chollet; Josef Urban (NIPS, 2016)

DeepMath - Deep Sequence Models for Premise Selection. - Alexander A. Alemi; François Chollet; Geoffrey Irving; Christian Szegedy; Josef Urban (CoRR, 2016)

Improved generator objectives for GANs. - Ben Poole; Alexander A. Alemi; Jascha Sohl-Dickstein; Anelia Angelova (CoRR, 2016)

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. - Christian Szegedy; Sergey Ioffe; Vincent Vanhoucke; Alexander A. Alemi (AAAI, 2017)

Deep Variational Information Bottleneck. - Alexander A. Alemi; Ian Fischer; Joshua V. Dillon; Kevin Murphy 002 (ICLR (Poster), 2017)

Motion Prediction Under Multimodality with Conditional Stochastic Networks. - Katerina Fragkiadaki; Jonathan Huang; Alex Alemi; Sudheendra Vijayanarasimhan; Susanna Ricco; Rahul Sukthankar (CoRR, 2017)

Jeffrey's prior sampling of deep sigmoidal networks. - Lorien X. Hayden; Alexander A. Alemi; Paul H. Ginsparg; James P. Sethna (CoRR, 2017)

Watch Your Step: Learning Graph Embeddings Through Attention. - Sami Abu-El-Haija; Bryan Perozzi; Rami Al-Rfou; Alex Alemi (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 002 (CoRR, 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)

Fixing a Broken ELBO. - Alexander A. Alemi; Ben Poole; Ian Fischer; Joshua V. Dillon; Rif A. Saurous; Kevin Murphy 002 (ICML, 2018)

Watch Your Step: Learning Node Embeddings via Graph Attention. - Sami Abu-El-Haija; Bryan Perozzi; Rami Al-Rfou; Alexander A. Alemi (NeurIPS, 2018)

GILBO: One Metric to Measure Them All. - Alexander A. Alemi; Ian Fischer (NeurIPS, 2018)

Uncertainty in the Variational Information Bottleneck. - Alexander A. Alemi; Ian Fischer; Joshua V. Dillon (CoRR, 2018)

TherML: Thermodynamics of Machine Learning. - Alexander A. Alemi; Ian Fischer (CoRR, 2018)

β-VAEs can retain label information even at high compression. - Emily Fertig; Aryan Arbabi; Alexander A. Alemi (CoRR, 2018)

Variational Predictive Information Bottleneck. - Alexander A. Alemi (AABI, 2019)

Information in Infinite Ensembles of Infinitely-Wide Neural Networks. - Ravid Shwartz-Ziv; Alexander A. Alemi (AABI, 2019)

On Variational Bounds of Mutual Information. - Ben Poole; Sherjil Ozair; Aäron van den Oord; Alexander A. Alemi; George Tucker (ICML, 2019)

On the Use of ArXiv as a Dataset. - Colin B. Clement; Matthew Bierbaum; Kevin P. O'Keeffe; Alexander A. Alemi (CoRR, 2019)

Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces. - Bryan Seybold; Emily Fertig; Alex Alemi; Ian Fischer (CoRR, 2019)

On Predictive Information Sub-optimality of RNNs. - Zhe Dong; Deniz Oktay; Ben Poole; Alexander A. Alemi (CoRR, 2019)

Thermodynamic Computing. - Tom Conte 001; 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; et al. (CoRR, 2019)

Neural Tangents: Fast and Easy Infinite Neural Networks in Python. - Roman Novak; Lechao Xiao; Jiri Hron; Jaehoon Lee 001; Alexander A. Alemi; Jascha Sohl-Dickstein; Samuel S. Schoenholz (ICLR, 2020)

PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. - Warren R. Morningstar; Alexander A. Alemi; Joshua V. Dillon (CoRR, 2020)

VIB is Half Bayes. - Alexander A. Alemi; Warren R. Morningstar; Ben Poole; Ian Fischer; Joshua V. Dillon (CoRR, 2020)

CEB Improves Model Robustness. - Ian Fischer; Alexander A. Alemi (Entropy, 2020)

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 (AISTATS, 2021)

Does Knowledge Distillation Really Work? - Samuel Stanton; Pavel Izmailov; Polina Kirichenko; Alexander A. Alemi; Andrew Gordon Wilson (NeurIPS, 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)

PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime. - Warren R. Morningstar; Alex Alemi; Joshua V. Dillon (AISTATS, 2022)

Bayesian Imitation Learning for End-to-End Mobile Manipulation. - Yuqing Du; Daniel Ho; Alex Alemi; Eric Jang; Mohi Khansari (ICML, 2022)

Weighted Ensemble Self-Supervised Learning. - Yangjun Ruan; Saurabh Singh; Warren Richard Morningstar; Alexander A. Alemi; Sergey Ioffe; Ian Fischer; Joshua V. Dillon (ICLR, 2023)

Variational Prediction. - Alexander A. Alemi; Ben Poole (CoRR, 2023)

Speed Limits for Deep Learning. - Inbar Seroussi; Alexander A. Alemi; Moritz Helias; Zohar Ringel (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; et al. (CoRR, 2023)

Small-scale proxies for large-scale Transformer training instabilities. - Mitchell Wortsman; Peter J. Liu; Lechao Xiao; Katie E. Everett; Alexander A. Alemi; Ben Adlam; John D. Co-Reyes; Izzeddin Gur; Abhishek Kumar; Roman Novak; Jeffrey Pennington; Jascha Sohl-Dickstein; et al. (ICLR, 2024)

Scaling Exponents Across Parameterizations and Optimizers. - Katie E. Everett; Lechao Xiao; Mitchell Wortsman; Alexander A. Alemi; Roman Novak; Peter J. Liu; Izzeddin Gur; Jascha Sohl-Dickstein; Leslie Pack Kaelbling; Jaehoon Lee 001; Jeffrey Pennington (ICML, 2024)

Training LLMs over Neurally Compressed Text. - Brian Lester; Jaehoon Lee 001; Alexander A. Alemi; Jeffrey Pennington; Adam Roberts; Jascha Sohl-Dickstein; Noah Constant (Trans. Mach. Learn. Res., 2024)

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 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)