Search Results for author:
Found 6 papers, 1 papers with code
Managing Diversity in Airbnb Search
One of the long-standing questions in search systems is the role of diversity in results.
Improving Deep Learning For Airbnb Search
In this paper we describe the journey beyond, discussing what we refer to as the ABCs of improving search: A for architecture, B for bias and C for cold start.
Applying Deep Learning To Airbnb Search
The application to search ranking is one of the biggest machine learning success stories at Airbnb.
Supervised Semantic Gradient Extraction Using Linear-Time Optimization
This paper proposes a new supervised semantic edge and gradient extraction approach, which allows the user to roughly scribble over the desired region to extract semantically-dominant and coherent edges in it.
Unsupervised Template Learning for Fine-Grained Object Recognition
It differs from recognition of basic categories, such as humans, tables, and computers, in that there are global similarities in shape or structure shared within a category, and the differences are in the details of the object parts.
Structure Learning for Optimization
We describe a family of global optimization procedures that automatically decompose optimization problems into smaller loosely coupled problems, then combine the solutions of these with message passing algorithms.
