no code implementations • 11 May 2023 • Kun Su, Judith Yue Li, Qingqing Huang, Dima Kuzmin, Joonseok Lee, Chris Donahue, Fei Sha, Aren Jansen, Yu Wang, Mauro Verzetti, Timo I. Denk
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures.
no code implementations • 8 Feb 2023 • Qingqing Huang, Daniel S. Park, Tao Wang, Timo I. Denk, Andy Ly, Nanxin Chen, Zhengdong Zhang, Zhishuai Zhang, Jiahui Yu, Christian Frank, Jesse Engel, Quoc V. Le, William Chan, Zhifeng Chen, Wei Han
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts.
Ranked #2 on Text-to-Music Generation on MusicCaps
3 code implementations • 26 Jan 2023 • Andrea Agostinelli, Timo I. Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, Matt Sharifi, Neil Zeghidour, Christian Frank
We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff".
Ranked #8 on Text-to-Music Generation on MusicCaps
no code implementations • 9 Jan 2023 • Judith Yue Li, Aren Jansen, Qingqing Huang, Joonseok Lee, Ravi Ganti, Dima Kuzmin
Multimodal learning can benefit from the representation power of pretrained Large Language Models (LLMs).
1 code implementation • 26 Aug 2022 • Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, Daniel P. W. Ellis
Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries.
no code implementations • 12 Apr 2022 • Kevin Kilgour, Beat Gfeller, Qingqing Huang, Aren Jansen, Scott Wisdom, Marco Tagliasacchi
The second model, SoundFilter, takes a mixed source audio clip as an input and separates it based on a conditioning vector from the shared text-audio representation defined by SoundWords, making the model agnostic to the conditioning modality.
no code implementations • 11 Feb 2020 • John Anderson, Qingqing Huang, Walid Krichene, Steffen Rendle, Li Zhang
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids.
no code implementations • 20 Nov 2019 • Ivan Vendrov, Tyler Lu, Qingqing Huang, Craig Boutilier
Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational.
no code implementations • 21 Feb 2016 • Qingqing Huang, Sham M. Kakade, Weihao Kong, Gregory Valiant
When can accurate reconstruction be accomplished in the sparse data regime?
no code implementations • NeurIPS 2015 • Qingqing Huang, Sham M. Kakade
- The number of measurements taken by and the computational complexity of our algorithm are bounded by a polynomial in both the number of points k and the dimension d, with no dependence on the separation \Delta.
no code implementations • 2 Mar 2015 • Rong Ge, Qingqing Huang, Sham M. Kakade
Unfortunately, learning mixture of Gaussians is an information theoretically hard problem: in order to learn the parameters up to a reasonable accuracy, the number of samples required is exponential in the number of Gaussian components in the worst case.
no code implementations • 13 Nov 2014 • Qingqing Huang, Rong Ge, Sham Kakade, Munther Dahleh
Consider a stationary discrete random process with alphabet size d, which is assumed to be the output process of an unknown stationary Hidden Markov Model (HMM).