Elad Eban | DeepAI

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Featured Co-authors

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∙ 12/03/2020

Multiple Networks are More Efficient than One: Fast and Accurate Models via Ensembles and Cascades

Recent work on efficient neural network architectures focuses on discove...

0 Xiaofang Wang, et al. ∙

∙ 10/02/2020

Neighbourhood Distillation: On the benefits of non end-to-end distillation

End-to-end training with back propagation is the standard method for tra...

0 Laëtitia Shao, et al. ∙

∙ 06/17/2020

Fine-Grained Stochastic Architecture Search

State-of-the-art deep networks are often too large to deploy on mobile d...

0 Shraman Ray Chaudhuri, et al. ∙

∙ 06/15/2020

Sky Optimization: Semantically aware image processing of skies in low-light photography

The sky is a major component of the appearance of a photograph, and its ...

0 Orly Liba, et al. ∙

∙ 12/18/2019

Computationally Efficient Neural Image Compression

Image compression using neural networks have reached or exceeded non-neu...

0 Nick Johnston, et al. ∙

∙ 11/25/2019

Structured Multi-Hashing for Model Compression

Despite the success of deep neural networks (DNNs), state-of-the-art mod...

19 Elad Eban, et al. ∙

∙ 10/04/2018

Seq2Slate: Re-ranking and Slate Optimization with RNNs

Ranking is a central task in machine learning and information retrieval....

12 Irwan Bello, et al. ∙

∙ 02/28/2018

Constrained Classification and Ranking via Quantiles

In most machine learning applications, classification accuracy is not th...

0 Alan Mackey, et al. ∙

∙ 11/18/2017

MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks

We present MorphNet, an approach to automate the design of neural networ...

0 Ariel Gordon, et al. ∙

∙ 09/26/2013

Learning Max-Margin Tree Predictors

Structured prediction is a powerful framework for coping with joint pred...

0 Ofer Meshi, et al. ∙

∙ 06/18/2012

Learning the Experts for Online Sequence Prediction

Online sequence prediction is the problem of predicting the next element...

0 Elad Eban, et al. ∙

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