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Daniel D. Johnson 0001
Person information
- affiliation: Google Research
- affiliation (former): Harvey Mudd College, Claremont, CA, USA
Other persons with the same name
- Daniel D. Johnson 0002 — University of Michigan, USA
- Daniel D. Johnson 0004 — AT&T Bell Laboratories, USA
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2020 – today
- 2024
- [j2]Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. Trans. Mach. Learn. Res. 2024 (2024) - [c12]Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison:
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. ICML 2024 - [i12]Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison:
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. CoRR abs/2402.08733 (2024) - [i11]Daniel D. Johnson:
Penzai + Treescope: A Toolkit for Interpreting, Visualizing, and Editing Models As Data. CoRR abs/2408.00211 (2024) - 2023
- [c11]Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison:
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. ICLR 2023 - [c10]Daniel D. Johnson, Daniel Tarlow, Christian Walder:
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents. ICML 2023: 15262-15306 - [i10]Daniel D. Johnson, Daniel Tarlow, Christian Walder:
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents. CoRR abs/2303.00732 (2023) - [i9]Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. CoRR abs/2311.14115 (2023) - 2022
- [i8]David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent J. Hellendoorn, Daniel D. Johnson, Daniel Tarlow:
A Library for Representing Python Programs as Graphs for Machine Learning. CoRR abs/2208.07461 (2022) - [i7]Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison:
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. CoRR abs/2210.01883 (2022) - 2021
- [j1]Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin:
Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming. Proc. ACM Program. Lang. 5(ICFP): 1-29 (2021) - [c9]Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg:
Structured Denoising Diffusion Models in Discrete State-Spaces. NeurIPS 2021: 17981-17993 - [c8]Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan:
Learning Generalized Gumbel-max Causal Mechanisms. NeurIPS 2021: 26792-26803 - [i6]Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin:
Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming. CoRR abs/2104.05372 (2021) - [i5]Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg:
Structured Denoising Diffusion Models in Discrete State-Spaces. CoRR abs/2107.03006 (2021) - [i4]Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow:
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models. CoRR abs/2107.07675 (2021) - [i3]Ningning Xie, Daniel D. Johnson, Dougal Maclaurin, Adam Paszke:
Parallel Algebraic Effect Handlers. CoRR abs/2110.07493 (2021) - [i2]Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan:
Learning Generalized Gumbel-max Causal Mechanisms. CoRR abs/2111.06888 (2021) - 2020
- [c7]Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow:
Learning Graph Structure With A Finite-State Automaton Layer. NeurIPS 2020 - [i1]Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow:
Learning Graph Structure With A Finite-State Automaton Layer. CoRR abs/2007.04929 (2020)
2010 – 2019
- 2018
- [c6]Satyan L. Devadoss, Daniel D. Johnson, Justin Lee, Jackson Warley:
Geometric Realizations of the 3D Associahedron (Multimedia Exposition). SoCG 2018: 75:1-75:4 - [c5]Daniel D. Johnson, Daniel Gorelik, Ross E. Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing, Cody Gabriel, Peter Sankhagowit:
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments. NeurIPS 2018: 3469-3478 - 2017
- [c4]Daniel D. Johnson:
Generating Polyphonic Music Using Tied Parallel Networks. EvoMUSART 2017: 128-143 - [c3]Daniel D. Johnson, Robert M. Keller, Nicholas Weintraut:
Learning to Create Jazz Melodies Using a Product of Experts. ICCC 2017: 151-158 - [c2]Daniel D. Johnson:
Learning Graphical State Transitions. ICLR 2017 - 2016
- [c1]Maxwell Waugaman, Zakkai Davidson, Samuel Dietrich, Daniel D. Johnson, Cassandra Meyer, Eric Storm, Avi Thaker, Ivan Wong:
LEG processor for education. EWME 2016: 1-5
Coauthor Index
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last updated on 2024-10-22 21:15 CEST by the dblp team
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