Daniel D. Johnson

According to our database, Daniel D. Johnson authored at least 17 papers between 2016 and 2024.

Collaborative distances:

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

2024

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs.

Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

CoRR, 2024

2023

A density estimation perspective on learning from pairwise human preferences.

Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin

CoRR, 2023

R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents.

Daniel D. Johnson, Daniel Tarlow, Christian Walder

Proceedings of the International Conference on Machine Learning, 2023

Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions.

Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

A Library for Representing Python Programs as Graphs for Machine Learning.

David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent J. Hellendoorn, Daniel D. Johnson, Daniel Tarlow

CoRR, 2022

2021

Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming.

Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin

Proc. ACM Program. Lang., 2021

Parallel Algebraic Effect Handlers.

Ningning Xie, Daniel D. Johnson, Dougal Maclaurin, Adam Paszke

CoRR, 2021

Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models.

Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow

CoRR, 2021

Learning Generalized Gumbel-max Causal Mechanisms.

Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan

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

Structured Denoising Diffusion Models in Discrete State-Spaces.

Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg

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

2020

Learning Graph Structure With A Finite-State Automaton Layer.

Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018

Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments.

Daniel D. Johnson, Daniel Gorelik, Ross E. Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing, Cody Gabriel, Peter Sankhagowit

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

Geometric Realizations of the 3D Associahedron (Multimedia Exposition).

Satyan L. Devadoss, Daniel D. Johnson, Justin Lee, Jackson Warley

Proceedings of the 34th International Symposium on Computational Geometry, 2018

2017

Learning Graphical State Transitions.

Daniel D. Johnson

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

Learning to Create Jazz Melodies Using a Product of Experts.

Daniel D. Johnson, Robert M. Keller, Nicholas Weintraut

Proceedings of the Eighth International Conference on Computational Creativity, 2017

Generating Polyphonic Music Using Tied Parallel Networks.

Daniel D. Johnson

Proceedings of the Computational Intelligence in Music, Sound, Art and Design, 2017

2016

LEG processor for education.

Maxwell Waugaman, Zakkai Davidson, Samuel Dietrich, Daniel D. Johnson, Cassandra Meyer, Eric Storm, Avi Thaker, Ivan Wong

Proceedings of the 11th European Workshop on Microelectronics Education, 2016