Zobrazeno 1 - 10
of 19 299
pro vyhledávání: '"A. McAuley"'
Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. Although effective, GR models operate in a transductive setting, meaning they can
Externí odkaz:
http://arxiv.org/abs/2410.02939
Modeling temporal characteristics plays a significant role in the representation learning of audio waveform. We propose Contrastive Long-form Language-Audio Pretraining (\textbf{CoLLAP}) to significantly extend the perception window for both the inpu
Externí odkaz:
http://arxiv.org/abs/2410.02271
Recent years have seen many audio-domain text-to-music generation models that rely on large amounts of text-audio pairs for training. However, symbolic-domain controllable music generation has lagged behind partly due to the lack of a large-scale sym
Externí odkaz:
http://arxiv.org/abs/2410.02084
Despite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with surrounding objects, remains a substantial challenge. Two critical factors in assimilating thes
Externí odkaz:
http://arxiv.org/abs/2410.00487
Despite recent advancements in language and vision modeling, integrating rich multimodal knowledge into recommender systems continues to pose significant challenges. This is primarily due to the need for efficient recommendation, which requires adapt
Externí odkaz:
http://arxiv.org/abs/2409.16627
Autor:
Yao, Yuhang, Zhang, Jianyi, Wu, Junda, Huang, Chengkai, Xia, Yu, Yu, Tong, Zhang, Ruiyi, Kim, Sungchul, Rossi, Ryan, Li, Ang, Yao, Lina, McAuley, Julian, Chen, Yiran, Joe-Wong, Carlee
Large language models are rapidly gaining popularity and have been widely adopted in real-world applications. While the quality of training data is essential, privacy concerns arise during data collection. Federated learning offers a solution by allo
Externí odkaz:
http://arxiv.org/abs/2409.15723
Autor:
Wang, Yu, Han, Chi, Wu, Tongtong, He, Xiaoxin, Zhou, Wangchunshu, Sadeq, Nafis, Chen, Xiusi, He, Zexue, Wang, Wei, Haffari, Gholamreza, Ji, Heng, McAuley, Julian
Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central to this is enabling continuous, high-frequency interactions, where the
Externí odkaz:
http://arxiv.org/abs/2409.13265
Autor:
Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Korikov, Anton, Sanner, Scott, Ramisa, Arnau, Vidal, Rene, Sathiamoorthy, Maheswaran, Kasrizadeh, Atoosa, Milano, Silvia, Ricci, Francesco
Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models have gained prominence in machine learning due to the development of app
Externí odkaz:
http://arxiv.org/abs/2409.15173
Autor:
Ramisa, Arnau, Vidal, Rene, Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Korikov, Anton, Sanner, Scott, Sathiamoorthy, Mahesh, Kasrizadeh, Atoosa, Milano, Silvia, Ricci, Francesco
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance. With the advent of generative AI, users have come to expect richer levels of
Externí odkaz:
http://arxiv.org/abs/2409.10993
The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and large prestige companies. Such issues highlight the need for publicly av
Externí odkaz:
http://arxiv.org/abs/2409.10831