ML4MD: Machine Learning for Media Discovery |
Website | https://sites.google.com/view/ml4md2020 |
Submission link | https://easychair.org/conferences/?conf=ml4md0 |
Abstract registration deadline | June 12, 2020 |
Submission deadline | June 12, 2020 |
Camera-Ready | July 12, 2020 |
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.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The papers must follow the extended abstract format of ICML:
- 2-page single-blind submission using this LaTeX template: https://media.icml.cc/Conferences/ICML2020/Styles/icml2020_style.zip
List of Topics
- 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
- Evaluation methodology
- 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
- Pattern discovery
Committees
Program Committee
- Erik M. Schmidt, Netflix
- Oriol Nieto, Pandora
- Fabien Gouyon, Pandora
- Yves Raimond, Netflix
- Katherine M. Kinnaird, Smith College
- More TBD
Organizing committee
- Erik M. Schmidt, Netflix
- Oriol Nieto, Pandora
- Fabien Gouyon, Pandora
- Yves Raimond, Netflix
- Katherine M. Kinnaird, Smith College
- Gert Lanckriet, Amazon and UCSD
Invited Speakers
- Jure Leskovec, Stanford University and Pinterest
- Ed Chi, Google Brain
- Eva Zangerle, Universität Innsbruck
- Matthias Mauch, Apple Music
- Delia Fano Yela, Chordify
Contact
All questions about submissions should be emailed to onieto@pandora.com and/or eschmidt@netflix.com
Sponsors
Netflix, Pandora, and Amazon