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Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing

Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing

20 January 2025
David Perera
Victor Letzelter
Théo Mariotte
Adrien Cortés
Mickaël Chen
S. Essid
Ga¨el Richard
ArXivPDFHTML

Papers citing "Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing"

5 / 5 papers shown
Title
Multiple Choice Learning for Efficient Speech Separation with Many
  Speakers
Multiple Choice Learning for Efficient Speech Separation with Many Speakers
David Perera
François Derrida
Théo Mariotte
Gaël Richard
S. Essid
52
0
0
27 Nov 2024
Annealed Winner-Takes-All for Motion Forecasting
Annealed Winner-Takes-All for Motion Forecasting
Yihong Xu
Victor Letzelter
Mickaël Chen
Éloi Zablocki
Matthieu Cord
26
1
0
17 Sep 2024
DiverseNet: When One Right Answer is not Enough
DiverseNet: When One Right Answer is not Enough
Michael Firman
Neill D. F. Campbell
Lourdes Agapito
Gabriel J. Brostow
3DH
OOD
26
24
0
24 Aug 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,635
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
245
9,042
0
06 Jun 2015
1