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1506.04147
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On the accuracy of self-normalized log-linear models

v1v2 (latest)

On the accuracy of self-normalized log-linear models

Neural Information Processing Systems (NeurIPS), 2015

12 June 2015

Maxim Rabinovich

ArXiv (abs)PDF HTML

Papers citing "On the accuracy of self-normalized log-linear models"

5 / 5 papers shown

Distributionally Robust Models with Parametric Likelihood Ratios

Distributionally Robust Models with Parametric Likelihood RatiosInternational Conference on Learning Representations (ICLR), 2022

Tatsunori Hashimoto

Graham Neubig

276

21

0

13 Apr 2022

Analyzing and Improving the Optimization Landscape of Noise-Contrastive
Estimation

Analyzing and Improving the Optimization Landscape of Noise-Contrastive EstimationInternational Conference on Learning Representations (ICLR), 2021

Pradeep Ravikumar

Andrej Risteski

316

19

0

21 Oct 2021

Von Mises-Fisher Loss for Training Sequence to Sequence Models with
Continuous Outputs

Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs

273

77

0

10 Dec 2018

Dropout with Expectation-linear Regularization

Dropout with Expectation-linear Regularization

254

53

0

26 Sep 2016

BlackOut: Speeding up Recurrent Neural Network Language Models With Very
Large Vocabularies

BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies

S.V.N. Vishwanathan

Michael J. Anderson

485

77

0

21 Nov 2015

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