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Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics

10 August 2023

Papers citing "Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics"

21 / 21 papers shown

Title
Differentiable Collision Detection: a Randomized Smoothing Approach Louis Montaut Quentin Le Lidec Antoine Bambade Vladimir Petrik Josef Sivic Justin Carpentier 78 29 0 19 Sep 2022
Autoinverse: Uncertainty Aware Inversion of Neural Networks Navid Ansari Hans-Peter Seidel Nima Vahidi Ferdowsi Vahid Babaei BDL 57 13 0 29 Aug 2022
Node Graph Optimization Using Differentiable Proxies Yiwei Hu Paul Guerrero Miloš Hašan Holly Rushmeier Valentin Deschaintre 75 25 0 15 Jul 2022
Gradients are Not All You Need Luke Metz C. Freeman S. Schoenholz Tal Kachman 76 93 0 10 Nov 2021
Differentiable Rendering with Perturbed Optimizers Quentin Le Lidec Ivan Laptev Cordelia Schmid Justin Carpentier 57 15 0 18 Oct 2021
Bundled Gradients through Contact via Randomized Smoothing H.J. Terry Suh Tao Pang Russ Tedrake 124 54 0 11 Sep 2021
PyGAD: An Intuitive Genetic Algorithm Python Library A. Gad ODL GP AI4CE 43 192 0 11 Jun 2021
Modular Primitives for High-Performance Differentiable Rendering S. Laine Janne Hellsten Tero Karras Yeongho Seol J. Lehtinen Timo Aila 67 453 0 06 Nov 2020
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates Alex Renda Yishen Chen Charith Mendis Michael Carbin 35 36 0 08 Oct 2020
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains Matthew Tancik Pratul P. Srinivasan B. Mildenhall Sara Fridovich-Keil N. Raghavan Utkarsh Singhal R. Ramamoorthi Jonathan T. Barron Ren Ng 124 2,421 0 18 Jun 2020
A Novel Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization Jiaxin Zhang Hoang Tran Dan Lu Guannan Zhang 69 17 0 07 Feb 2020
Deep Parametric Indoor Lighting Estimation Marc-André Gardner Yannick Hold-Geoffroy Kalyan Sunkavalli Christian Gagné Jean-François Lalonde 3DPC 3DV 55 132 0 19 Oct 2019
DiffTaichi: Differentiable Programming for Physical Simulation Yuanming Hu Luke Anderson Tzu-Mao Li Qi Sun N. Carr Jonathan Ragan-Kelley F. Durand 63 388 0 01 Oct 2019
Learning Surrogate Losses Josif Grabocka Randolf Scholz Lars Schmidt-Thieme 54 42 0 24 May 2019
On the Spectral Bias of Neural Networks Nasim Rahaman A. Baratin Devansh Arpit Felix Dräxler Min Lin Fred Hamprecht Yoshua Bengio Aaron Courville 154 1,451 0 22 Jun 2018
Flexible Neural Representation for Physics Prediction Damian Mrowca Chengxu Zhuang E. Wang Nick Haber Li Fei-Fei J. Tenenbaum Daniel L. K. Yamins OCL AI4CE 70 249 0 21 Jun 2018
Neural 3D Mesh Renderer Hiroharu Kato Yoshitaka Ushiku Tatsuya Harada 3DV 73 1,046 0 20 Nov 2017
On Nesting Monte Carlo Estimators Tom Rainforth R. Cornish Hongseok Yang Andrew Warrington Frank Wood 99 132 0 18 Sep 2017
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 384 441 0 01 Dec 2016
The CMA Evolution Strategy: A Tutorial N. Hansen 74 1,377 0 04 Apr 2016
Randomized Smoothing for Stochastic Optimization John C. Duchi Peter L. Bartlett Martin J. Wainwright 102 288 0 22 Mar 2011