8,482 research outputs found
Visual Search at eBay
In this paper, we propose a novel end-to-end approach for scalable visual
search infrastructure. We discuss the challenges we faced for a massive
volatile inventory like at eBay and present our solution to overcome those. We
harness the availability of large image collection of eBay listings and
state-of-the-art deep learning techniques to perform visual search at scale.
Supervised approach for optimized search limited to top predicted categories
and also for compact binary signature are key to scale up without compromising
accuracy and precision. Both use a common deep neural network requiring only a
single forward inference. The system architecture is presented with in-depth
discussions of its basic components and optimizations for a trade-off between
search relevance and latency. This solution is currently deployed in a
distributed cloud infrastructure and fuels visual search in eBay ShopBot and
Close5. We show benchmark on ImageNet dataset on which our approach is faster
and more accurate than several unsupervised baselines. We share our learnings
with the hope that visual search becomes a first class citizen for all large
scale search engines rather than an afterthought.Comment: To appear in 23rd SIGKDD Conference on Knowledge Discovery and Data
Mining (KDD), 2017. A demonstration video can be found at
https://youtu.be/iYtjs32vh4
Scalable Privacy-Compliant Virality Prediction on Twitter
The digital town hall of Twitter becomes a preferred medium of communication
for individuals and organizations across the globe. Some of them reach
audiences of millions, while others struggle to get noticed. Given the impact
of social media, the question remains more relevant than ever: how to model the
dynamics of attention in Twitter. Researchers around the world turn to machine
learning to predict the most influential tweets and authors, navigating the
volume, velocity, and variety of social big data, with many compromises. In
this paper, we revisit content popularity prediction on Twitter. We argue that
strict alignment of data acquisition, storage and analysis algorithms is
necessary to avoid the common trade-offs between scalability, accuracy and
privacy compliance. We propose a new framework for the rapid acquisition of
large-scale datasets, high accuracy supervisory signal and multilanguage
sentiment prediction while respecting every privacy request applicable. We then
apply a novel gradient boosting framework to achieve state-of-the-art results
in virality ranking, already before including tweet's visual or propagation
features. Our Gradient Boosted Regression Tree is the first to offer
explainable, strong ranking performance on benchmark datasets. Since the
analysis focused on features available early, the model is immediately
applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective
Content Analysi
Serving deep learning models in a serverless platform
Serverless computing has emerged as a compelling paradigm for the development
and deployment of a wide range of event based cloud applications. At the same
time, cloud providers and enterprise companies are heavily adopting machine
learning and Artificial Intelligence to either differentiate themselves, or
provide their customers with value added services. In this work we evaluate the
suitability of a serverless computing environment for the inferencing of large
neural network models. Our experimental evaluations are executed on the AWS
Lambda environment using the MxNet deep learning framework. Our experimental
results show that while the inferencing latency can be within an acceptable
range, longer delays due to cold starts can skew the latency distribution and
hence risk violating more stringent SLAs
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
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