Dieterich Lawson

24 papers A* 4Misc 1Journal 11Unranked 8

Year	Rank	Type	Title / Venue / Authors
2023	A*	`conf`	NAS-X: Neural Adaptive Smoothing via Twisting. NeurIPS Dieterich Lawson, Michael Li, Scott W. Linderman
2023	J	`jnl`	NAS-X: Neural Adaptive Smoothing via Twisting. CoRR Dieterich Lawson, Michael Li, Scott W. Linderman
2022	A*	`conf`	SIXO: Smoothing Inference with Twisted Objectives. NeurIPS Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman
2022	J	`jnl`	SIXO: Smoothing Inference with Twisted Objectives. CoRR Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman
2022	A*	`conf`	The Neural Testbed: Evaluating Joint Predictions. NeurIPS Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Dieterich Lawson, Botao Hao, Brendan O'Donoghue, Benjamin Van Roy
2021	J	`jnl`	Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? CoRR Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O'Donoghue, Benjamin Van Roy
2019	—	`conf`	Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
2019	A*	`conf`	Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2019	J	`jnl`	Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2019	—	`conf`	Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
2018	J	`jnl`	Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
2018	Misc	`conf`	Learning Hard Alignments with Variational Inference. ICASSP Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly
2017	J	`jnl`	An online sequence-to-sequence model for noisy speech recognition. CoRR Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly
2017	J	`jnl`	Changing Model Behavior at Test-Time Using Reinforcement Learning. CoRR Augustus Odena, Dieterich Lawson, Christopher Olah
2017	—	`conf`	Changing Model Behavior at Test-time Using Reinforcement Learning. ICLR (Workshop) Augustus Odena, Dieterich Lawson, Christopher Olah
2017	—	`conf`	Filtering Variational Objectives. NIPS Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
2017	J	`jnl`	Filtering Variational Objectives. CoRR Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
2017	J	`jnl`	Learning Hard Alignments with Variational Inference. CoRR Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly
2017	—	`conf`	Particle Value Functions. ICLR (Workshop) Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
2017	J	`jnl`	Particle Value Functions. CoRR Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
2017	—	`conf`	REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein
2017	—	`conf`	Training a Subsampling Mechanism in Expectation. ICLR (Workshop) Colin Raffel, Dieterich Lawson
2017	J	`jnl`	Training a Subsampling Mechanism in Expectation. CoRR Colin Raffel, Dieterich Lawson
2011	—	`conf`	The tablecast data publishing protocol. ISCRAM Ka-Ping Yee, Dieterich Lawson, Dominic König, Dale Zak