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Convex Distillation: Efficient Compression of Deep Networks via Convex
  Optimization

Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization

9 October 2024
Prateek Varshney
Mert Pilanci
ArXivPDFHTML

Papers citing "Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization"

21 / 21 papers shown
Title
Convexifying Transformers: Improving optimization and understanding of
  transformer networks
Convexifying Transformers: Improving optimization and understanding of transformer networks
Tolga Ergen
Behnam Neyshabur
Harsh Mehta
MLT
81
15
0
20 Nov 2022
FitHuBERT: Going Thinner and Deeper for Knowledge Distillation of Speech
  Self-Supervised Learning
FitHuBERT: Going Thinner and Deeper for Knowledge Distillation of Speech Self-Supervised Learning
Yeonghyeon Lee
Kangwook Jang
Jahyun Goo
Youngmoon Jung
Hoi-Rim Kim
95
33
0
01 Jul 2022
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
Aaron Mishkin
Arda Sahiner
Mert Pilanci
OffRL
116
30
0
02 Feb 2022
Distilling Knowledge via Knowledge Review
Distilling Knowledge via Knowledge Review
Pengguang Chen
Shu Liu
Hengshuang Zhao
Jiaya Jia
186
440
0
19 Apr 2021
The Pitfalls of Simplicity Bias in Neural Networks
The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah
Kaustav Tamuly
Aditi Raghunathan
Prateek Jain
Praneeth Netrapalli
AAML
65
359
0
13 Jun 2020
Natural Language Processing Advancements By Deep Learning: A Survey
Natural Language Processing Advancements By Deep Learning: A Survey
A. Torfi
Rouzbeh A. Shirvani
Yaser Keneshloo
Nader Tavvaf
Edward A. Fox
AI4CE
VLM
125
220
0
02 Mar 2020
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex
  Optimization Formulations for Two-layer Networks
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks
Mert Pilanci
Tolga Ergen
77
118
0
24 Feb 2020
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks
  Trained with the Logistic Loss
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss
Lénaïc Chizat
Francis R. Bach
MLT
114
339
0
11 Feb 2020
Evolution of Image Segmentation using Deep Convolutional Neural Network:
  A Survey
Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey
F. Sultana
Abu Sufian
P. Dutta
SSeg
62
256
0
13 Jan 2020
AI Benchmark: All About Deep Learning on Smartphones in 2019
AI Benchmark: All About Deep Learning on Smartphones in 2019
Andrey D. Ignatov
Radu Timofte
Andrei Kulik
Seungsoo Yang
Ke Wang
Felix Baum
Max Wu
Lirong Xu
Luc Van Gool
ELM
43
220
0
15 Oct 2019
Autonomous Navigation via Deep Reinforcement Learning for Resource
  Constraint Edge Nodes using Transfer Learning
Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learning
Aqeel Anwar
A. Raychowdhury
55
73
0
12 Oct 2019
Survey on Deep Neural Networks in Speech and Vision Systems
Survey on Deep Neural Networks in Speech and Vision Systems
M. Alam
Manar D. Samad
Lasitha Vidyaratne
Alexander M. Glandon
Khan M. Iftekharuddin
3DV
VLM
AI4TS
68
210
0
16 Aug 2019
Similarity-Preserving Knowledge Distillation
Similarity-Preserving Knowledge Distillation
Frederick Tung
Greg Mori
118
977
0
23 Jul 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan
Quoc V. Le
3DV
MedIm
137
18,115
0
28 May 2019
Searching for MobileNetV3
Searching for MobileNetV3
Andrew G. Howard
Mark Sandler
Grace Chu
Liang-Chieh Chen
Bo Chen
...
Yukun Zhu
Ruoming Pang
Vijay Vasudevan
Quoc V. Le
Hartwig Adam
345
6,778
0
06 May 2019
Benchmark Analysis of Representative Deep Neural Network Architectures
Benchmark Analysis of Representative Deep Neural Network Architectures
Simone Bianco
Rémi Cadène
Luigi Celona
Paolo Napoletano
BDL
66
677
0
01 Oct 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle
Michael Carbin
225
3,463
0
09 Mar 2018
Learning both Weights and Connections for Efficient Neural Networks
Learning both Weights and Connections for Efficient Neural Networks
Song Han
Jeff Pool
J. Tran
W. Dally
CVBM
310
6,672
0
08 Jun 2015
FitNets: Hints for Thin Deep Nets
FitNets: Hints for Thin Deep Nets
Adriana Romero
Nicolas Ballas
Samira Ebrahimi Kahou
Antoine Chassang
C. Gatta
Yoshua Bengio
FedML
303
3,883
0
19 Dec 2014
Compressing Deep Convolutional Networks using Vector Quantization
Compressing Deep Convolutional Networks using Vector Quantization
Yunchao Gong
Liu Liu
Ming Yang
Lubomir D. Bourdev
MQ
155
1,170
0
18 Dec 2014
Microsoft COCO: Common Objects in Context
Microsoft COCO: Common Objects in Context
Nayeon Lee
Michael Maire
Serge J. Belongie
Lubomir Bourdev
Ross B. Girshick
James Hays
Pietro Perona
Deva Ramanan
C. L. Zitnick
Piotr Dollár
ObjD
413
43,638
0
01 May 2014
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