Dawen Liang
Dawen Liang
Research Scientist, Netflix Inc.
Verified email at - Homepage
Cited by
Cited by
librosa: Audio and music signal analysis in python.
B McFee, C Raffel, D Liang, DPW Ellis, M McVicar, E Battenberg, O Nieto
SciPy, 18-24, 2015
Variational autoencoders for collaborative filtering
D Liang, RG Krishnan, MD Hoffman, T Jebara
Proceedings of the 2018 World Wide Web Conference, 689-698, 2018
mir_eval: a transparent implementation of common MIR metrics
C Raffel, B McFee, EJ Humphrey, J Salamon, O Nieto, D Liang, DPW Ellis
ISMIR, 367-372, 2014
Modeling user exposure in recommendation
D Liang, L Charlin, J McInerney, DM Blei
Proceedings of the 25th international conference on World Wide Web, 951-961, 2016
Edward: A library for probabilistic modeling, inference, and criticism
D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei
arXiv preprint arXiv:1610.09787, 2016
Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence
D Liang, J Altosaar, L Charlin, DM Blei
Proceedings of the 10th ACM conference on recommender systems, 59-66, 2016
Causal inference for recommender systems
Y Wang, D Liang, L Charlin, DM Blei
Proceedings of the 14th ACM Conference on Recommender Systems, 426-431, 2020
Deep learning for recommender systems: A Netflix case study
H Steck, L Baltrunas, E Elahi, D Liang, Y Raimond, J Basilico
AI Magazine 42 (3), 7-18, 2021
Causal inference for recommendation
D Liang, L Charlin, DM Blei
Causation: Foundation to Application, Workshop at UAI. AUAI 6 (41), 108, 2016
Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks
D Liang, M Zhan, DPW Ellis
ISMIR, 295-301, 2015
On the challenges of learning with inference networks on sparse, high-dimensional data
R Krishnan, D Liang, M Hoffman
International conference on artificial intelligence and statistics, 143-151, 2018
The deconfounded recommender: A causal inference approach to recommendation
Y Wang, D Liang, L Charlin, DM Blei
arXiv preprint arXiv:1808.06581, 2018
Large language models as zero-shot conversational recommenders
Z He, Z Xie, R Jha, H Steck, D Liang, Y Feng, BP Majumder, N Kallus, ...
Proceedings of the 32nd ACM international conference on information and …, 2023
librosa 0.5. 0
B McFee, M McVicar, O Nieto, S Balke, C Thome, D Liang, E Battenberg, ...
Zenodo. URL: https://doi. org/10 5281, 2017
librosa: 0.4. 1
B McFee, M McVicar, C Raffel, D Liang, O Nieto, E Battenberg, J Moore, ...
Zenodo, 2015
Beta Process Sparse Nonnegative Matrix Factorization for Music
D Liang, MD Hoffman, DPW Ellis
ISMIR, 375-380, 2013
Methods and prospects for human–computer performance of popular music
RB Dannenberg, NE Gold, D Liang, G Xia
Computer Music Journal 38 (2), 36-50, 2014
Music genre classification with the million song dataset
D Liang, H Gu, B O’Connor
Machine Learning Department, CMU, 2011
Active scores: Representation and synchronization in human–computer performance of popular music
RB Dannenberg, NE Gold, D Liang, G Xia
Computer Music Journal 38 (2), 51-62, 2014
Correlated variational auto-encoders
D Tang, D Liang, T Jebara, N Ruozzi
International Conference on Machine Learning, 6135-6144, 2019
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