Deep Neural Network Training with Frank-Wolfe

14 October 2020

Papers citing "Deep Neural Network Training with Frank-Wolfe"

24 / 24 papers shown

Title
PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs Max Zimmer Megi Andoni Christoph Spiegel Sebastian Pokutta VLM 81 10 0 23 Dec 2023
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 117 36 0 29 Apr 2023
Projection-Free Adaptive Gradients for Large-Scale Optimization Cyrille W. Combettes Christoph Spiegel Sebastian Pokutta ODL 49 11 0 29 Sep 2020
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers Robin M. Schmidt Frank Schneider Philipp Hennig ODL 69 164 0 03 Jul 2020
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization Geoffrey Negiar Gideon Dresdner Alicia Y. Tsai L. Ghaoui Francesco Locatello Robert M. Freund Fabian Pedregosa 37 24 0 27 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 261 42,038 0 03 Dec 2019
Rigging the Lottery: Making All Tickets Winners Utku Evci Trevor Gale Jacob Menick Pablo Samuel Castro Erich Elsen 136 592 0 25 Nov 2019
One Sample Stochastic Frank-Wolfe Mingrui Zhang Zebang Shen Aryan Mokhtari Hamed Hassani Amin Karbasi 57 58 0 10 Oct 2019
Momentum-Based Variance Reduction in Non-Convex SGD Ashok Cutkosky Francesco Orabona ODL 67 402 0 24 May 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio Léon Bottou UQCV DRL 40 112 0 11 Dec 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 81 572 0 04 Jul 2018
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization Aryan Mokhtari Hamed Hassani Amin Karbasi 44 113 0 24 Apr 2018
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity Lin Chen Christopher Harshaw Hamed Hassani Amin Karbasi 39 76 0 22 Feb 2018
Learning Sparse Neural Networks through $L_0$ Regularization Christos Louizos Max Welling Diederik P. Kingma 236 1,136 0 04 Dec 2017
Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap Aryan Mokhtari S. Hassani Amin Karbasi 36 72 0 05 Nov 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 168 8,807 0 25 Aug 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 48 1,023 0 23 May 2017
Conditional Accelerated Lazy Stochastic Gradient Descent Guanghui Lan Sebastian Pokutta Yi Zhou Daniel Zink 65 27 0 16 Mar 2017
Training Sparse Neural Networks Suraj Srinivas Akshayvarun Subramanya R. Venkatesh Babu 90 207 0 21 Nov 2016
Stochastic Frank-Wolfe Methods for Nonconvex Optimization Sashank J. Reddi S. Sra Barnabás Póczós Alex Smola 52 140 0 27 Jul 2016
Variance-Reduced and Projection-Free Stochastic Optimization Elad Hazan Haipeng Luo 43 165 0 05 Feb 2016
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 171 18,534 0 06 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 813 149,474 0 22 Dec 2014
Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application Weiran Wang M. A. Carreira-Perpiñán 58 287 0 06 Sep 2013