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Daniel D. Johnson
Daniel D. Johnson
Research Scientist, Google DeepMind / University of Toronto
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Structured Denoising Diffusion Models in Discrete State-Spaces
J Austin, D Johnson, J Ho, D Tarlow, R Berg
Advances in Neural Information Processing Systems, 2021, 2021
5632021
Generating Polyphonic Music Using Tied Parallel Networks
DD Johnson
International Conference on Evolutionary and Biologically Inspired Music and …, 2017
1362017
Learning Graphical State Transitions
DD Johnson
International Conference on Learning Representations, 2017
119*2017
Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming
A Paszke, DD Johnson, D Duvenaud, D Vytiniotis, A Radul, MJ Johnson, ...
Proceedings of the ACM on Programming Languages 5 (ICFP), 1-29, 2021
492021
Learning to Create Jazz Melodies Using a Product of Experts
DD Johnson, RM Keller, N Weintraut
Eighth International Conference on Computational Creativity, ICCC, Atlanta, 2017, 0
27*
Learning Graph Structure With A Finite-State Automaton Layer
DD Johnson, H Larochelle, D Tarlow
Advances in Neural Information Processing Systems 33, 2020
192020
Learning Generalized Gumbel-max Causal Mechanisms
G Lorberbom, DD Johnson, CJ Maddison, D Tarlow, T Hazan
Advances in Neural Information Processing Systems 34, 26792-26803, 2021
162021
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
DD Johnson, J Austin, R van den Berg, D Tarlow
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2021
162021
Contrastive learning can find an optimal basis for approximately view-invariant functions
DD Johnson, A El Hanchi, CJ Maddison
The Eleventh International Conference on Learning Representations, 2022
152022
RU-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
DD Johnson, D Tarlow, C Walder
arXiv preprint arXiv:2303.00732, 2023
42023
A Library for Representing Python Programs as Graphs for Machine Learning
D Bieber, K Shi, P Maniatis, C Sutton, V Hellendoorn, D Johnson, ...
arXiv preprint arXiv:2208.07461, 2022
32022
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
DD Johnson, D Tarlow, D Duvenaud, CJ Maddison
arXiv preprint arXiv:2402.08733, 2024
22024
Parallel Algebraic Effect Handlers
N Xie, DD Johnson, D Maclaurin, A Paszke
arXiv preprint arXiv:2110.07493, 2021
22021
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments
D Johnson, D Gorelik, RE Mawhorter, K Suver, W Gu, S Xing, C Gabriel, ...
Advances in Neural Information Processing Systems, 3465-3474, 2018
22018
A density estimation perspective on learning from pairwise human preferences
V Dumoulin, DD Johnson, PS Castro, H Larochelle, Y Dauphin
arXiv preprint arXiv:2311.14115, 2023
12023
LEG processor for education
M Waugaman, Z Davidson, S Dietrich, D Johnson, C Meyer, E Storm, ...
2016 11th European Workshop on Microelectronics Education (EWME), 1-5, 2016
12016
Machine-learned models for generating code snippets with predicted placeholders for optimizing software development
DDW Johnson, DS Tarlow, M Tabachnyk, MH Rasi, J Austin, ...
US Patent 11,972,234, 2024
2024
Geometric Realizations of the 3D Associahedron (Multimedia Exposition)
SL Devadoss, DD Johnson, J Lee, J Warley
34th International Symposium on Computational Geometry (SoCG 2018), 2018
2018
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Articles 1–18