"Lossless" Compression of Deep Neural Networks: A High-dimensional
Neural Tangent Kernel Approach

"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach

1 March 2024

Yuan Zhang

Papers citing ""Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach"

6 / 6 papers shown

Title
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption Vasilii Feofanov Malik Tiomoko Aladin Virmaux 31 5 0 20 Oct 2023
On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint Zenan Ling Zhenyu Liao Robert C. Qiu 32 0 0 31 Aug 2023
Random matrices in service of ML footprint: ternary random features with no performance loss Hafiz Tiomoko Ali Zhenyu Liao Romain Couillet 36 7 0 05 Oct 2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 139 684 0 31 Jan 2021
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 220 348 0 14 Jun 2018
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 950 20,561 0 17 Apr 2017