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Geoffrey Roeder
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2020 – today
- 2023
- [c6]Xingyuan Sun
, Geoffrey Roeder
, Tianju Xue
, Ryan P. Adams
, Szymon Rusinkiewicz
:
More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. SCF 2023: 8:1-8:14 - 2022
- [i8]Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz:
More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. CoRR abs/2205.16008 (2022) - [i7]Faris M. Sbahi
, Antonio J. Martinez, Sahil Patel, Dmitri Saberi, Jae Hyeon Yoo, Geoffrey Roeder, Guillaume Verdon:
Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry. CoRR abs/2206.04663 (2022) - 2021
- [c5]Geoffrey Roeder, Luke Metz, Durk Kingma:
On Linear Identifiability of Learned Representations. ICML 2021: 9030-9039 - [i6]Jacob Miller, Geoffrey Roeder, Tai-Danae Bradley:
Probabilistic Graphical Models and Tensor Networks: A Hybrid Framework. CoRR abs/2106.15666 (2021) - 2020
- [c4]Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams:
Learning Composable Energy Surrogates for PDE Order Reduction. NeurIPS 2020 - [i5]Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams:
Learning Composable Energy Surrogates for PDE Order Reduction. CoRR abs/2005.06549 (2020) - [i4]Geoffrey Roeder, Luke Metz, Diederik P. Kingma:
On Linear Identifiability of Learned Representations. CoRR abs/2007.00810 (2020)
2010 – 2019
- 2019
- [c3]Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds:
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. ICML 2019: 4445-4455 - [i3]Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds:
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. CoRR abs/1905.12090 (2019) - 2018
- [c2]Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud:
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. ICLR (Poster) 2018 - 2017
- [c1]Geoffrey Roeder, Yuhuai Wu, David Duvenaud:
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. NIPS 2017: 6925-6934 - [i2]Geoffrey Roeder, Yuhuai Wu, David Duvenaud:
Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. CoRR abs/1703.09194 (2017) - [i1]Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud:
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR abs/1711.00123 (2017)
Coauthor Index
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