Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to friend recommendation in social networks.
Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance.
Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification.
Inspired by the fact that successive CNN layers represent the image with increasing levels of abstraction, we compressed our deep ranking model to a single CNN by coupling activations from multiple intermediate layers along with the last layer.
Ranked #2 on Image Retrieval on street2shop - topwear
In this paper, we propose a deep convolutional neural network for learning the embeddings of images in order to capture the notion of visual similarity.
Ranked #1 on Image Retrieval on street2shop - topwear
Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications.
We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images.
In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel.
We show experimentally that IST results in training time that are much lower than data parallel approaches to distributed learning, and that it scales to large models that cannot be learned using standard approaches.