Machine Learning for Media Discovery Workshop
Jure Leskovec, Stanford University and Pinterest
Ed Chi, Google Brain
Eva Zangerle, Universität Innsbruck
Matthias Mauch, Apple Music
Delia Fano Yela, Chordify
Call For Papers
The ever-increasing size and accessibility of vast media libraries has created a demand more than ever for AI-based systems that are capable of organizing, recommending, and understanding such complex data.
While this topic has received only limited attention within the core machine learning community, it has been an area of intense focus within the applied communities such as the Recommender Systems (RecSys), Music Information Retrieval (MIR), and Computer Vision communities. At the same time, these domains have surfaced nebulous problem spaces and rich datasets that are of tremendous potential value to machine learning and the AI communities at large.
This year's Machine Learning for Media Discovery (ML4MD) aims to build upon the five previous Machine Learning for Music Discovery editions at ICML, broadening the topic area from music discovery to media discovery. The added topic diversity is aimed towards having a broader conversation with the machine learning community and to offer cross-pollination across the various media domains.
We invite the research community, from both industry and academia, to submit 2-page extended abstracts on topics such as:
Media (including Music, Movies, Podcasts, Series, etc.) recommendation and discovery
Media recommendation explainability at scale
Content-based and multimodal media recommender systems
Transfer learning and semi-supervised learning for media discovery
Fairness in recommendations
Bandits and reinforcement learning for media recommendations
Audio, video, image, and semantic content-based machine learning
Deep learning applications for computational audio and video research
Browsing and visualization of media datasets
Similarity metric learning
Learning to rank
Modeling ambiguity and preference in media
Software frameworks and tools for deep learning in media
Automatic classification of media
AI-based media creation and machine creativity
Automatic media composition and improvisation
Media feature extraction
Two page single-blind abstract submissions should be formatted according to the ICML template:
Note that you will need to modify the template to use the accepted version of the template for authors to appear.
Only papers using the above template will be considered. Word templates will not be provided. Review is single-blind. A third page may be used for references only.
Papers should be submitted via EasyChair:
For posters we will follow the standard ICML requirements (24" x 36" in portrait orientation).
All questions about submissions should be emailed to Erik Schmidt (firstname.lastname@example.org) and/or Oriol Nieto (email@example.com).
Two-page abstracts deadline:
May 1, 2020 June 5, 2020 June 12, 2020
Notification of Acceptance:
May 9, 2020 June 22, 2020
Videos for oral presentation:
June 30, 2020
July 1, 2020 July 12, 2020
Workshop Date: July 18, 2020
This year's event will be a livestream run by SlidesLive. Presenters that are selected for oral presentation will receive a link in order to record their presentation. The recording will capture both video of the presenter as well as the slides. We have a quick turnaround between acceptance and the video due date because SlidesLive requires time before the event for editing. During their recent work for the ICLR conference they received many poor quality and incomplete videos. We ask that presenters take extra care to consider camera position, lighting, and audio quality to make things easy on them. Documentation will be provided closer to the event.
Registration for ML4MD is done through ICML. This year ICML is charging a flat rate for access to all ICML events. The rate is $100 for participants or $25 for students.
Justin Basilico, Netflix
Ching-Wei Chen, Spotify
Sander Dieleman, Google DeepMind
James McInerney, Netflix
Filip Korzeniowski, Pandora
Mark Levy, Apple
Matt McCallum, Pandora
Matt McVicar, Apple
Vito Ostuni, Pandora
Justin Salamon, Adobe
Erik M. Schmidt, Netflix
Oriol Nieto, Pandora
Fabien Gouyon, Pandora
Yves Raimond, Netflix
Katherine M. Kinnaird, Smith College
Gert Lanckriet, Amazon and UCSD