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ORCIDDieterich Lawson
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2020 – today
- 2023
[c13]Dieterich Lawson, Michael Li, Scott W. Linderman:
NAS-X: Neural Adaptive Smoothing via Twisting. NeurIPS 2023- [i11]Dieterich Lawson, Michael Li, Scott W. Linderman:
NAS-X: Neural Adaptive Smoothing via Twisting. CoRR abs/2308.14864 (2023) - 2022
- [c12]Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman:
SIXO: Smoothing Inference with Twisted Objectives. NeurIPS 2022 - [c11]Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Dieterich Lawson, Botao Hao, Brendan O'Donoghue, Benjamin Van Roy:
The Neural Testbed: Evaluating Joint Predictions. NeurIPS 2022 - [i10]Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman:
SIXO: Smoothing Inference with Twisted Objectives. CoRR abs/2206.05952 (2022) - 2021
- [i9]Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O'Donoghue, Benjamin Van Roy:
Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? CoRR abs/2110.04629 (2021)
2010 – 2019
- 2019
- [c10]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR 2019 - [c9]George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison:
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) 2019 - [c8]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS 2019: 8499-8511 - [i8]Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath:
Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR abs/1910.14265 (2019) - 2018
- [c7]Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly:
Learning Hard Alignments with Variational Inference. ICASSP 2018: 5799-5803 - [i7]George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison:
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR abs/1810.04152 (2018) - 2017
- [c6]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. ICLR (Workshop) 2017 - [c5]Augustus Odena, Dieterich Lawson, Christopher Olah:
Changing Model Behavior at Test-time Using Reinforcement Learning. ICLR (Workshop) 2017 - [c4]Colin Raffel, Dieterich Lawson:
Training a Subsampling Mechanism in Expectation. ICLR (Workshop) 2017 - [c3]George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS 2017: 2627-2636 - [c2]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. NIPS 2017: 6573-6583 - [i6]Colin Raffel, Dieterich Lawson:
Training a Subsampling Mechanism in Expectation. CoRR abs/1702.06914 (2017) - [i5]Augustus Odena, Dieterich Lawson, Christopher Olah:
Changing Model Behavior at Test-Time Using Reinforcement Learning. CoRR abs/1702.07780 (2017) - [i4]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. CoRR abs/1703.05820 (2017) - [i3]Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly:
Learning Hard Alignments with Variational Inference. CoRR abs/1705.05524 (2017) - [i2]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. CoRR abs/1705.09279 (2017) - [i1]Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly:
An online sequence-to-sequence model for noisy speech recognition. CoRR abs/1706.06428 (2017) - 2011
- [c1]Ka-Ping Yee, Dieterich Lawson, Dominic König, Dale Zak:
The tablecast data publishing protocol. ISCRAM 2011
Coauthor Index
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