Search Results for author:
Found 10 papers, 4 papers with code
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
We present MorphNet, an approach to automate the design of neural network structures.
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval.
Fine-Grained Stochastic Architecture Search
Mobile neural architecture search (NAS) methods automate the design of small models but state-of-the-art NAS methods are expensive to run.
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 color and tone can strongly influence the mood of a picture.
Constrained Classification and Ranking via Quantiles
The maximization of many of these metrics can be expressed as a constrained optimization problem, where the constraint is a function of the classifier's predictions.
Learning Max-Margin Tree Predictors
We demonstrate the effectiveness of our approach on several domains and show that, despite the relative simplicity of the structure, prediction accuracy is competitive with a fully connected model that is computationally costly at prediction time.
Structured Multi-Hashing for Model Compression
Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory.
Computationally Efficient Neural Image Compression
Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG).
Neighbourhood Distillation: On the benefits of non end-to-end distillation
Instead of distilling a model end-to-end, we propose to split it into smaller sub-networks - also called neighbourhoods - that are then trained independently.
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models
Committee-based models (ensembles or cascades) construct models by combining existing pre-trained ones.
