Nips 2016 Spotlight A Consistent

Media Summary: On Regularizing Rademacher Observation Losses, presented at

Nips 2016 Spotlight A Consistent - Detailed Analysis & Overview

On Regularizing Rademacher Observation Losses, presented at

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NIPS 2016 - Spotlight: A Consistent Regularization Approach for Structured Prediction

NIPS 2016 Finding significant combinations of features in the presence of categorical covariates

NIPS 2016 Spotlight - Unsupervised Learning for Physical Interaction through Video Prediction

[NIPS 2016] W. Wen, at el, Learning Structured Sparsity in Deep Neural Networks

NIPS 2016, On Regularizing Rademacher Observation Losses

Collaborative Recurrent Autoencoder for Recommender Systems - NIPS 2016 spotlight video

NIPS 2016 spotlight: Adaptive Averaging in Accelerated Descent Dynamics

Interpretable Nonlinear Dynamic Modeling of Neural Trajectories [NIPS 2016 spotlight]

NIPS 2016 Spotlight video Dense Associative Memory for Pattern Recognition

NIPS 2016 spotlight - PAC Bayesian Theory Meets Bayesian Inference

Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint (NIPS 2016)

NIPS 2016 - Spotlight Video - Simon-Gabriel et al.

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NIPS 2016 - Spotlight: A Consistent Regularization Approach for Structured Prediction

NIPS 2016 - Spotlight: A Consistent Regularization Approach for Structured Prediction

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NIPS 2016 Finding significant combinations of features in the presence of categorical covariates

NIPS 2016 Finding significant combinations of features in the presence of categorical covariates

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NIPS 2016 Spotlight - Unsupervised Learning for Physical Interaction through Video Prediction

NIPS 2016 Spotlight - Unsupervised Learning for Physical Interaction through Video Prediction

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[NIPS 2016] W. Wen, at el, Learning Structured Sparsity in Deep Neural Networks

[NIPS 2016] W. Wen, at el, Learning Structured Sparsity in Deep Neural Networks

Spotlight

NIPS 2016, On Regularizing Rademacher Observation Losses

NIPS 2016, On Regularizing Rademacher Observation Losses

On Regularizing Rademacher Observation Losses, presented at

Collaborative Recurrent Autoencoder for Recommender Systems - NIPS 2016 spotlight video

Collaborative Recurrent Autoencoder for Recommender Systems - NIPS 2016 spotlight video

NIPS 2016 spotlight

NIPS 2016 spotlight: Adaptive Averaging in Accelerated Descent Dynamics

NIPS 2016 spotlight: Adaptive Averaging in Accelerated Descent Dynamics

Spotlight

Interpretable Nonlinear Dynamic Modeling of Neural Trajectories [NIPS 2016 spotlight]

Interpretable Nonlinear Dynamic Modeling of Neural Trajectories [NIPS 2016 spotlight]

This is a 3-min

NIPS 2016 Spotlight video Dense Associative Memory for Pattern Recognition

NIPS 2016 Spotlight video Dense Associative Memory for Pattern Recognition

This is a

NIPS 2016 spotlight - PAC Bayesian Theory Meets Bayesian Inference

NIPS 2016 spotlight - PAC Bayesian Theory Meets Bayesian Inference

NIPS 2016 spotlight

Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint (NIPS 2016)

Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint (NIPS 2016)

Spotlight

NIPS 2016 - Spotlight Video - Simon-Gabriel et al.

NIPS 2016 - Spotlight Video - Simon-Gabriel et al.

This is a 3 minutes

NIPS 2016 Spotlight - Boosting with Abstention

NIPS 2016 Spotlight - Boosting with Abstention

For more details, please refer to paper: http://www.cims.nyu.edu/~desalvo/files/boostingwithabstension.pdf.