research
∙
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...
share
research
∙
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...
research
∙
06/17/2020
Fine-Grained Stochastic Architecture Search
State-of-the-art deep networks are often too large to deploy on mobile d...
research
∙
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 ...
research
∙
12/18/2019
Computationally Efficient Neural Image Compression
Image compression using neural networks have reached or exceeded non-neu...
research
∙
11/25/2019
Structured Multi-Hashing for Model Compression
Despite the success of deep neural networks (DNNs), state-of-the-art mod...
research
∙
10/04/2018
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval....
research
∙
02/28/2018
Constrained Classification and Ranking via Quantiles
In most machine learning applications, classification accuracy is not th...
research
∙
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...
research
∙
09/26/2013
Learning Max-Margin Tree Predictors
Structured prediction is a powerful framework for coping with joint pred...
research
∙
06/18/2012
