Geoffrey Roeder

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 14

Top Topics

Variational Inference

Backpropagation Neural Networks

Top Venues

Publications

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)
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 (2022)
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 (2022)
Jacob Miller, Geoffrey Roeder, Tai-Danae Bradley
Probabilistic Graphical Models and Tensor Networks: A Hybrid Framework. CoRR (2021)
Geoffrey Roeder, Luke Metz, Durk Kingma
On Linear Identifiability of Learned Representations. ICML (2021)
Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
Learning Composable Energy Surrogates for PDE Order Reduction. CoRR (2020)
Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
Learning Composable Energy Surrogates for PDE Order Reduction. NeurIPS (2020)
Geoffrey Roeder, Luke Metz, Diederik P. Kingma
On Linear Identifiability of Learned Representations. CoRR (2020)
Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. CoRR (2019)
Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. ICML (2019)
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)
Geoffrey Roeder, Yuhuai Wu, David Duvenaud
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. NIPS (2017)
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR (2017)
Geoffrey Roeder, Yuhuai Wu, David Duvenaud
Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. CoRR (2017)