Quick Context: Video for the paper "Self-Supervised Intrinsic Image Decomposition" by Michael Janner, Jiajun Wu, Tejas D. This is a 3-minute video for the paper "Discovering Potential Correlations via Hypercontractivity" (

Attentivechrome Nips 2017 -

Video for the paper "Self-Supervised Intrinsic Image Decomposition" by Michael Janner, Jiajun Wu, Tejas D. This is a 3-minute video for the paper "Discovering Potential Correlations via Hypercontractivity" ( Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee (Spotlight video for

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  • Video for the paper "Self-Supervised Intrinsic Image Decomposition" by Michael Janner, Jiajun Wu, Tejas D.
  • This is a 3-minute video for the paper "Discovering Potential Correlations via Hypercontractivity" (
  • Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee (Spotlight video for
  • Tolstikhin, Gelly, Bousquet, Simon-Gabriel, Schoelkopf AdaGAN: Boosting Generative Models
  • Streaming Weak Submodularity: Interperting Neural Networks on the Fly Ethan R.

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Supporting Images

AttentiveChrome NIPS 2017
NIPS 2017 Spotlight Video - Structured Embedding Models for Grouped Data
[NIPS 2017] Discovering Potential Correlations via Hypercontractivity
Self-Supervised Intrinsic Image Decomposition, NIPS 2017
AdaGAN: Boosting Generative Models (NIPS 2017)
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly (NIPS Preview Video)
Net Trim (NIPS 2017)
Acceleration and Averaging in Stochastic Descent Dynamics - NIPS 2017 spotlight
NIPS 2017, Online to offline conversions, Universality & Adaptive minbatch sizes
NIPS 2017 Spotlight: Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues
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AttentiveChrome NIPS 2017

AttentiveChrome NIPS 2017

Read more details and related context about AttentiveChrome NIPS 2017.

NIPS 2017 Spotlight Video - Structured Embedding Models for Grouped Data

NIPS 2017 Spotlight Video - Structured Embedding Models for Grouped Data

Maja R. Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei link to the paper: code is on ...

[NIPS 2017] Discovering Potential Correlations via Hypercontractivity

[NIPS 2017] Discovering Potential Correlations via Hypercontractivity

This is a 3-minute video for the paper "Discovering Potential Correlations via Hypercontractivity" (

Self-Supervised Intrinsic Image Decomposition, NIPS 2017

Self-Supervised Intrinsic Image Decomposition, NIPS 2017

Video for the paper "Self-Supervised Intrinsic Image Decomposition" by Michael Janner, Jiajun Wu, Tejas D. Kulkarni, Ilker ...

AdaGAN: Boosting Generative Models (NIPS 2017)

AdaGAN: Boosting Generative Models (NIPS 2017)

Tolstikhin, Gelly, Bousquet, Simon-Gabriel, Schoelkopf AdaGAN: Boosting Generative Models

Streaming Weak Submodularity: Interpreting Neural Networks on the Fly (NIPS Preview Video)

Streaming Weak Submodularity: Interpreting Neural Networks on the Fly (NIPS Preview Video)

Streaming Weak Submodularity: Interperting Neural Networks on the Fly Ethan R. Elenberg, Alexandros G. Dimakis, Moran ...

Net Trim (NIPS 2017)

Net Trim (NIPS 2017)

Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee (Spotlight video for

Acceleration and Averaging in Stochastic Descent Dynamics - NIPS 2017 spotlight

Acceleration and Averaging in Stochastic Descent Dynamics - NIPS 2017 spotlight

Read more details and related context about Acceleration and Averaging in Stochastic Descent Dynamics - NIPS 2017 spotlight.

NIPS 2017, Online to offline conversions, Universality & Adaptive minbatch sizes

NIPS 2017, Online to offline conversions, Universality & Adaptive minbatch sizes

Read more details and related context about NIPS 2017, Online to offline conversions, Universality & Adaptive minbatch sizes.

NIPS 2017 Spotlight: Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues

NIPS 2017 Spotlight: Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues

Read more details and related context about NIPS 2017 Spotlight: Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues.