Gradients are Not All You Need

10 November 2021

Papers citing "Gradients are Not All You Need"

50 / 68 papers shown

Title
Locally Orderless Images for Optimization in Differentiable Rendering Ishit Mehta Manmohan Chandraker Ravi Ramamoorthi 29 0 0 27 Mar 2025
Gradient Networks Shreyas Chaudhari Srinivasa Pranav J. M. F. Moura 50 0 0 28 Jan 2025
Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation Juntao Dai Yaodong Yang Qian Zheng Gang Pan OffRL 76 2 0 15 Dec 2024
Multilingual Topic Classification in X: Dataset and Analysis Dimosthenis Antypas Asahi Ushio Francesco Barbieri Jose Camacho-Collados 32 0 0 04 Oct 2024
Autonomous Vehicle Controllers From End-to-End Differentiable Simulation Asen Nachkov Danda Pani Paudel Luc Van Gool 27 0 0 12 Sep 2024
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics Paul Fuchs Stephan Thaler Sebastien Röcken J. Zavadlav DiffM 61 5 0 28 Aug 2024
Narrowing the Focus: Learned Optimizers for Pretrained Models Gus Kristiansen Mark Sandler A. Zhmoginov Nolan Miller Anirudh Goyal Jihwan Lee Max Vladymyrov 34 1 0 17 Aug 2024
Joint-perturbation simultaneous pseudo-gradient Carlos Martin Tuomas Sandholm 27 2 0 17 Aug 2024
Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics Yuang Zhang Yu Hu Yunlong Song Danping Zou Weiyao Lin 36 17 0 15 Jul 2024
Behaviour Distillation Andrei Lupu Chris Xiaoxuan Lu Jarek Liesen R. T. Lange Jakob Foerster DD 39 4 0 21 Jun 2024
AlphaZeroES: Direct score maximization outperforms planning loss minimization Carlos Martin Tuomas Sandholm 13 0 0 12 Jun 2024
From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers Swaminathan Gurumurthy Karnik Ram Bingqing Chen Zachary Manchester Zico Kolter 45 1 0 12 Jun 2024
Mollification Effects of Policy Gradient Methods Tao Wang Sylvia L. Herbert Sicun Gao 33 0 0 28 May 2024
Stabilizing Backpropagation Through Time to Learn Complex Physics Patrick Schnell Nils Thuerey 35 2 0 03 May 2024
SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers Vandad Davoodnia Saeed Ghorbani Alexandre Messier Ali Etemad 33 1 0 19 Apr 2024
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs Yogesh Verma Markus Heinonen Vikas K. Garg AI4CE 21 26 0 15 Apr 2024
Learning Quadruped Locomotion Using Differentiable Simulation Yunlong Song Sangbae Kim Davide Scaramuzza 32 10 0 21 Mar 2024
Discovering Temporally-Aware Reinforcement Learning Algorithms Matthew Jackson Chris Xiaoxuan Lu Louis Kirsch R. T. Lange Shimon Whiteson Jakob N. Foerster 19 18 0 08 Feb 2024
Do Transformer World Models Give Better Policy Gradients? Michel Ma Tianwei Ni Clement Gehring P. DÓro Pierre-Luc Bacon 34 4 0 07 Feb 2024
Gradient Informed Proximal Policy Optimization Sanghyun Son L. Zheng Ryan Sullivan Yi-Ling Qiao Ming-Chyuan Lin 32 7 0 14 Dec 2023
Differentiable Visual Computing for Inverse Problems and Machine Learning Andrew Spielberg Fangcheng Zhong Konstantinos Rematas Krishna Murthy Jatavallabhula Cengiz Öztireli Tzu-Mao Li Derek Nowrouzezahrai 32 7 0 21 Nov 2023
Using Cooperative Game Theory to Prune Neural Networks M. Diaz-Ortiz Benjamin Kempinski Daphne Cornelisse Yoram Bachrach Tal Kachman 33 2 0 17 Nov 2023
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications Robert Tjarko Lange Yujin Tang Yingtao Tian ELM 38 3 0 04 Nov 2023
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms Shenao Zhang Boyi Liu Zhaoran Wang Tuo Zhao 8 2 0 30 Oct 2023
Fractal Landscapes in Policy Optimization Tao Wang Sylvia L. Herbert Sicun Gao 16 5 0 24 Oct 2023
Stabilizing RNN Gradients through Pre-training Luca Herranz-Celotti Jean Rouat 24 0 0 23 Aug 2023
Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics Michael Fischer Tobias Ritschel 14 2 0 10 Aug 2023
Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models T. Westenbroek Jacob Levy David Fridovich-Keil 12 0 0 16 Jul 2023
Effective Latent Differential Equation Models via Attention and Multiple Shooting German Abrevaya Mahta Ramezanian-Panahi Jean-Christophe Gagnon-Audet Pablo Polosecki Irina Rish S. Dawson Guillermo Cecchi G. Dumas MedIm 11 1 0 11 Jul 2023
Learning Space-Time Continuous Neural PDEs from Partially Observed States V. Iakovlev Markus Heinonen Harri Lähdesmäki 6 1 0 09 Jul 2023
Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control Dan Johnson Ronald Fedkiw AI4CE 21 2 0 21 Jun 2023
HUB: Guiding Learned Optimizers with Continuous Prompt Tuning Gaole Dai Wei Yu Wu Ziyu Wang Jie Fu Shanghang Zhang Tiejun Huang AIFin 9 0 0 26 May 2023
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single Paul Vicol Zico Kolter Kevin Swersky 6 6 0 21 Apr 2023
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li James Harrison Jascha Narain Sohl-Dickstein Virginia Smith Luke Metz 42 5 0 21 Apr 2023
Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search Andrei Kucharavy M. Monti R. Guerraoui Ljiljana Dolamic 32 1 0 20 Apr 2023
Inductive biases in deep learning models for weather prediction Jannik Thümmel Matthias Karlbauer S. Otte C. Zarfl Georg Martius ... Thomas Scholten Ulrich Friedrich V. Wulfmeyer B. Goswami Martin Volker Butz AI4CE 33 4 0 06 Apr 2023
Arbitrary Order Meta-Learning with Simple Population-Based Evolution Chris Xiaoxuan Lu Sebastian Towers Jakob N. Foerster 23 5 0 16 Mar 2023
Structured State Space Models for In-Context Reinforcement Learning Chris Xiaoxuan Lu Yannick Schroecker Albert Gu Emilio Parisotto Jakob N. Foerster Satinder Singh Feryal M. P. Behbahani AI4TS 86 81 0 07 Mar 2023
ApproxED: Approximate exploitability descent via learned best responses Carlos Martin T. Sandholm 11 0 0 20 Jan 2023
A Survey of Meta-Reinforcement Learning Jacob Beck Risto Vuorio E. Liu Zheng Xiong L. Zintgraf Chelsea Finn Shimon Whiteson OOD OffRL 30 121 0 19 Jan 2023
Human-Timescale Adaptation in an Open-Ended Task Space Adaptive Agent Team Jakob Bauer Kate Baumli Satinder Baveja Feryal M. P. Behbahani ... Jakub Sygnowski K. Tuyls Sarah York Alexander Zacherl Lei Zhang LM&Ro OffRL AI4CE LRM 30 108 0 18 Jan 2023
Differentiable Simulations for Enhanced Sampling of Rare Events Martin Sípka Johannes C. B. Dietschreit Lukáš Grajciar Rafael Gómez-Bombarelli 11 11 0 09 Jan 2023
evosax: JAX-based Evolution Strategies R. T. Lange 30 54 0 08 Dec 2022
Transformer-Based Learned Optimization Erik Gartner Luke Metz Mykhaylo Andriluka C. Freeman C. Sminchisescu 16 11 0 02 Dec 2022
Plateau-reduced Differentiable Path Tracing Michael Fischer Tobias Ritschel 16 9 0 30 Nov 2022
A survey of deep learning optimizers -- first and second order methods Rohan Kashyap ODL 16 6 0 28 Nov 2022
Discovering Evolution Strategies via Meta-Black-Box Optimization R. T. Lange Tom Schaul Yutian Chen Tom Zahavy Valenti Dallibard Chris Xiaoxuan Lu Satinder Singh Sebastian Flennerhag 33 47 0 21 Nov 2022
Adversarial Cheap Talk Chris Xiaoxuan Lu Timon Willi Alistair Letcher Jakob N. Foerster AAML 16 17 0 20 Nov 2022
Discovered Policy Optimisation Chris Xiaoxuan Lu J. Kuba Alistair Letcher Luke Metz Christian Schroeder de Witt Jakob N. Foerster OffRL 28 74 0 11 Oct 2022
Latent Neural ODEs with Sparse Bayesian Multiple Shooting V. Iakovlev Çağatay Yıldız Markus Heinonen Harri Lähdesmäki BDL 25 11 0 07 Oct 2022