Yair Movshovitz-Attias | DeepAI

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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. ∙

∙ 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. ∙

∙ 06/11/2018

Synthetic Depth-of-Field with a Single-Camera Mobile Phone

Shallow depth-of-field is commonly used by photographers to isolate a su...

0 Neal Wadhwa, et al. ∙

∙ 03/21/2017

No Fuss Distance Metric Learning using Proxies

We address the problem of distance metric learning (DML), defined as lea...

0 Yair Movshovitz-Attias, et al. ∙

∙ 03/26/2016

How useful is photo-realistic rendering for visual learning?

Data seems cheap to get, and in many ways it is, but the process of crea...

0 Yair Movshovitz-Attias, et al. ∙

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