Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Li, Judith"'
Autor:
Bao, Xuchan, Li, Judith Yue, Wan, Zhong Yi, Su, Kun, Denk, Timo, Lee, Joonseok, Kuzmin, Dima, Sha, Fei
Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users' diverse and uncertain retrieval needs. To address this limitation, we introduce Diff4Steer, a novel generative retrieval
Externí odkaz:
http://arxiv.org/abs/2412.04746
Autor:
Yang, Li, Subbiah, Anushya, Patel, Hardik, Li, Judith Yue, Song, Yanwei, Mirghaderi, Reza, Aggarwal, Vikram
Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modality to include image, audio, a
Externí odkaz:
http://arxiv.org/abs/2406.02844
Autor:
Su, Kun, Li, Judith Yue, Huang, Qingqing, Kuzmin, Dima, Lee, Joonseok, Donahue, Chris, Sha, Fei, Jansen, Aren, Wang, Yu, Verzetti, Mauro, Denk, Timo I.
Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures. While recent music generation models excel at the former through advanced audio codecs, the exploration of
Externí odkaz:
http://arxiv.org/abs/2305.06594
Autor:
Ram, Naveen, Kuzmin, Dima, Chio, Ellie Ka In, Alzantot, Moustafa Farid, Ontanon, Santiago, Jash, Ambarish, Li, Judith Yue
In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user's explicit preferences expressed in dialogue. While previous wor
Externí odkaz:
http://arxiv.org/abs/2305.06218
Multimodal learning can benefit from the representation power of pretrained Large Language Models (LLMs). However, state-of-the-art transformer based LLMs often ignore negations in natural language and there is no existing benchmark to quantitatively
Externí odkaz:
http://arxiv.org/abs/2301.03238
Autor:
Huang, Qingqing, Jansen, Aren, Lee, Joonseok, Ganti, Ravi, Li, Judith Yue, Ellis, Daniel P. W.
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. This paper presents MuLan: a first attempt at a new generation of acoustic mo
Externí odkaz:
http://arxiv.org/abs/2208.12415
Publikováno v:
Diversity and Distributions, 2022 Aug 01. 28(8), 1524-1541.
Externí odkaz:
https://www.jstor.org/stable/48678139
Publikováno v:
Plant and Soil, 2020 May 01. 450(1/2), 417-428.
Externí odkaz:
https://www.jstor.org/stable/48733324
Continuously tracking the movement of a fluid or a plume in the subsurface is a challenge that is often encountered in applications, such as tracking a plume of injected CO$_2$ or of a hazardous substance. Advances in monitoring techniques have made
Externí odkaz:
http://arxiv.org/abs/1404.3816