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Smooth Loss Functions for Deep Top-k Classification

Smooth Loss Functions for Deep Top-k Classification

21 February 2018
Leonard Berrada
Andrew Zisserman
M. P. Kumar
ArXiv (abs)PDFHTML

Papers citing "Smooth Loss Functions for Deep Top-k Classification"

7 / 57 papers shown
Title
Predicting Human Activities from User-Generated Content
Predicting Human Activities from User-Generated Content
Steven R. Wilson
Rada Mihalcea
13
9
0
19 Jul 2019
The Limited Multi-Label Projection Layer
The Limited Multi-Label Projection Layer
Brandon Amos
V. Koltun
J. Zico Kolter
95
36
0
20 Jun 2019
Differentiable Ranks and Sorting using Optimal Transport
Differentiable Ranks and Sorting using Optimal Transport
Marco Cuturi
O. Teboul
Jean-Philippe Vert
OT
91
159
0
28 May 2019
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat
  Examples Equally and Gradient Magnitude's Variance Matters
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters
Xinshao Wang
Yang Hua
Elyor Kodirov
David Clifton
N. Robertson
NoLa
139
63
0
28 Mar 2019
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Aditya Grover
Eric Wang
Aaron Zweig
Stefano Ermon
88
174
0
21 Mar 2019
On the Consistency of Top-k Surrogate Losses
On the Consistency of Top-k Surrogate Losses
Forest Yang
Oluwasanmi Koyejo
70
49
0
30 Jan 2019
Deep Frank-Wolfe For Neural Network Optimization
Deep Frank-Wolfe For Neural Network Optimization
Leonard Berrada
Andrew Zisserman
M. P. Kumar
ODL
64
40
0
19 Nov 2018
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