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Learning Explainable User Sentiment and Preferences for Information Filtering

Publications associées (153)

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Forged Authenticity: Governing Deepfake Risks

The manipulation of digital content is not a new phenomenon, but the application of machine learning to the creation of deepfakes has (i) radically improved the quality of output, (ii) slashed the resources required to produce realistic fakes at previously ...

EPFL International Risk Governance Center (IRGC)2019

PPML '19: Privacy Preserving Machine Learning

Carmela González Troncoso

The area of privacy preserving machine learning has been of growing importance in practice, which has lead to an increased interest in this topic in both academia and industry. We have witnessed this through numerous papers and systems published and develo ...

ACM2019

Privacy in Recommender Systems

Mahsa Taziki

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropria ...

EPFL2018

Bring voice user interfaces to our offices

Frederike Dümbgen

Synopsis: Implement a new way of interacting with your computer via voice control instead of the mouse and keyboard. Level:BS, MS Description: Google Home and Amazon Alexa are quickly rev ...

2018

Structuring Wikipedia Articles with Section Recommendations

Robert West, Tiziano Piccardi, Michele Catasta

Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for human ...

ASSOC COMPUTING MACHINERY2018

Diversifying Group Recommendation

Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Recommender-systems has been a significant research direction in both literature and practice. The core of recommender systems are the recommendation mechanisms, which suggest to a user a selected set of items supposed to match user true intent, based on e ...

2018

Blending Digital and Face-to-Face Interaction Using a Co -Located Social MediaApp in Class

Denis Gillet, Adrian Christian Holzer, Andrii Vozniuk, Sten Govaerts, Benoît Garbinato

Improving face-to-face (f2f) interaction in large classrooms is a challenging task as student participation can be hard to initiate. Thanks to the wide adoption of personal mobile devices, it is possible to blend digital and face-to-face interaction and in ...

IEEE COMPUTER SOC2018

Mapping affinities: visualizing academic practice through collaboration

Dario Rodighiero

Academic affinities are one of the most fundamental hidden dynamics that drive scientific development. Some affinities are actual, and consequently can be measured through classical academic metrics such as co-authoring. Other affinities are potential, and ...

EPFL2018

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Enhancing Social Media Platforms for Educational and Humanitarian Knowledge Sharing

Andrii Vozniuk

Social media (SM) platforms have demonstrated their ability to facilitate knowledge sharing on the global scale. They are increasingly often employed in educational and humanitarian domains where, despite their general benefits, they expose challenges pecu ...

EPFL2017