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8 papers · 1 filter
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis
Longqi Yang, Cheng-Kang Hsieh, Deborah Estrin
User preference profiling is an important task in modern online social networks (OSN). With the proliferation of image-centric social platforms, such as Pinterest, visual contents…
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Yin Cui, Feng Zhou, Yuanqing Lin +1
Existing fine-grained visual categorization methods often suffer from three challenges: lack of training data, large number of fine-grained categories, and high intraclass vs. low…
FairTest: Discovering Unwarranted Associations in Data-Driven Applications
Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu +5
In a world where traditional notions of privacy are increasingly challenged by the myriad companies that collect and analyze our data, it is important that decision-making entities…
Learning Concept Embeddings with Combined Human-Machine Expertise
Michael J. Wilber, Iljung S. Kwak, David Kriegman +1
This paper presents our work on "SNaCK," a low-dimensional concept embedding algorithm that combines human expertise with automatic machine similarity kernels. Both parts are compl…
Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences
Andreas Veit, Balazs Kovacs, Sean Bell +3
With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to an…
Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices
Nathan Kallus, Madeleine Udell
We consider the problem of learning the preferences of a heterogeneous population by observing choices from an assortment of products, ads, or other offerings. Our observation mode…