Program
Program Summary
ATTENDEES: ACCESS THE LIVESTREAM HERE
All times are in Pacific Daylight Time (PDT), i.e. US West Coast (you can check difference with your local time e.g. here)
9:00 - 9:10am: Welcome Remarks
9:10 - 10:20am: Oral Presentations
10:20 - 11:00am: Poster Session
11:00 - 12:20pm: Oral Presentations
12:20 - 1:00pm: Lunch Break
1:00 - 2:10pm: Oral Presentations
2:10 - 2:50pm: Poster Session
2:50 - 4:00pm: Oral Presentations
4:00-4:10pm: Closing Remarks
Invited presentations are 30 minutes each, accepted presentations are 20 minutes.
9:00 - 9:10am: Welcome Remarks
9:10 - 10:20am: Oral Presentations
Chair: Katherine KinnairdGraph Neural Networks for Reasoning over Multimodal Content
Jure Leskovec (Invited Speaker)Stanford University and PinterestNovel Audio Embeddings for Personalized Recommendations on Newly Released Tracks
Beici Liang, Zonghan Cai, Quan Chen, Yifan Li and Minwei GuTencent Music Entertainment[PDF]Musical Word Embedding: Bridging the Gap between Listening Contexts and Music
Seungheon Doh, Jongpil Lee, Tae Hong Park and Juhan NamKorea Advanced Institute of Science and Technology (KAIST) and New York University (NYU)[PDF]10:20 - 11:00am: Poster Session
Access poster Zoom links in ICML portal schedule here11:00 - 12:20pm: Oral Presentations
Chair: Fabien GouyonGraphs for music analysis : two examples
Delia Fano Yela (Invited Speaker)Chordify[PDF]Deep Active Learning Toward Crisis-related Tweets Classification
Shiva Ebrahimi and Xuan GuoUniversity of North Texas[PDF]The Unsung Heroes of Music Recommendation: an Essay
Matthias Mauch (Invited Speaker)Apple[PDF]12:20 - 1:00pm: Lunch Break
1:00 - 2:10pm: Oral Presentations
Chair: Yves RaimondBeyond Being Accurate: Solving Real-World Recommendation Problems
Ed Chi (Invited Speaker)Google ResearchCharacter-focused Video Thumbnail Retrieval
Shervin Ardeshir, Nagendra Kamath and Hossein TaghaviNetflix[PDF]HitPredict: Using Spotify Data to Predict Billboard Hits
Elena Georgieva, Marcella Suta and Nicholas BurtonStanford University[PDF]2:10 - 2:50pm: Poster Session
Access poster Zoom links in ICML portal schedule here2:50 - 4:00pm: Oral Presentations
Chair: Oriol NietoEva Zangerle (Invited Speaker)University of Innsbruck[PDF]I know why you like this movie: Interpretable Efficient Multimodal Recommender
Barbara Rychalska, Dominika Basaj, Jacek Dąbrowski and Michał DanilukSynerise and Warsaw University of Technology[PDF]Content-based Music Similarity with Siamese Networks
Joseph Cleveland, Derek Cheng, Michael Zhou, Thorsten Joachims and Douglas TurnbullIthaca College and Cornell University[PDF]Poster Presentations
Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach
Alexander Fang, Alisa Liu, Prem Seetharaman and Bryan PardoNorthwestern University[PDF] [Poster]Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
Alisa Liu, Alexander Fang, Gaëtan Hadjeres, Prem Seetharaman and Bryan PardoNorthwestern University and Sony CSL[PDF] [Poster]Artist biases in collaborative filtering for music recommendation
Andres Ferraro, Jae Ho Jeon, Biho Kim, Xavier Serra and Dmitry BogdanovUniversitat Pompeu Fabra and Kakao Corp[PDF] [Poster]Discovering X Degrees of Keyword Separation in a Fine Arts Collection
Arthur FlexerJohannes Kepler University Linz[PDF] [Poster]Generative Modelling for Controllable Audio Synthesis of Expressive Piano Performance
Hao Hao Tan, Yin-Jyun Luo and Dorien HerremansSingapore University of Technology and Design and Institute of High Performance Computing, A*STAR[PDF] [Poster]Web Interface for Exploration of Latent and Tag Spaces in Music Auto-Tagging
Philip Tovstogan, Xavier Serra and Dmitry BogdanovUniversitat Pompeu Fabra[PDF] [Poster]Cosine Similarity of Multimodal Content Vectors for TV Programmes
Saba Nazir, Taner Cagali, Chris Newell and Mehrnoosh SadrzadehUniversity College London, Queen Mary University of London, and British Broadcasting Company[PDF] [Poster]Self-Correcting Non-Chronological Autoregressive Music Generation
Wayne Chi, Prachi Kumar, Suri Yaddanapudi, Rahul Suresh and Umut IsikAmazon Web Services[PDF] [Poster]