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Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian
  Reparameterization offers Significant Performance and Efficiency Gains

Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains

26 September 2019
Sathya Ravi
Abhay Venkatesh
G. Fung
Vikas Singh
ArXivPDFHTML

Papers citing "Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains"

2 / 2 papers shown
Title
Physarum Powered Differentiable Linear Programming Layers and
  Applications
Physarum Powered Differentiable Linear Programming Layers and Applications
Zihang Meng
Sathya Ravi
Vikas Singh
23
5
0
30 Apr 2020
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
  Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan
Alex Kendall
R. Cipolla
SSeg
446
15,639
0
02 Nov 2015
1