Zobrazeno 1 - 10
of 5 300
pro vyhledávání: '"Mcauley, P."'
While sequential recommendation achieves significant progress on capturing user-item transition patterns, transferring such large-scale recommender systems remains challenging due to the disjoint user and item groups across domains. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2411.01785
Autor:
Wu, Junda, Li, Xintong, Wang, Ruoyu, Xia, Yu, Xiong, Yuxin, Wang, Jianing, Yu, Tong, Chen, Xiang, Kveton, Branislav, Yao, Lina, Shang, Jingbo, McAuley, Julian
Offline evaluation of LLMs is crucial in understanding their capacities, though current methods remain underexplored in existing research. In this work, we focus on the offline evaluation of the chain-of-thought capabilities and show how to optimize
Externí odkaz:
http://arxiv.org/abs/2410.23703
Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search. Recent studies also explore providing LLMs with initial retrieval results to generate query expansions more grounde
Externí odkaz:
http://arxiv.org/abs/2410.13765
Autor:
Shimizu, Ryotaro, Wada, Takashi, Wang, Yu, Kruse, Johannes, O'Brien, Sean, HtaungKham, Sai, Song, Linxin, Yoshikawa, Yuya, Saito, Yuki, Tsung, Fugee, Goto, Masayuki, McAuley, Julian
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between the predicted and ground-truth explanations. However, this approach fai
Externí odkaz:
http://arxiv.org/abs/2410.13248
Autor:
Xu, Weihan, Liang, Paul Pu, Kim, Haven, McAuley, Julian, Berg-Kirkpatrick, Taylor, Dong, Hao-Wen
Teasers are an effective tool for promoting content in entertainment, commercial and educational fields. However, creating an effective teaser for long videos is challenging for it requires long-range multimodal modeling on the input videos, while ne
Externí odkaz:
http://arxiv.org/abs/2410.05586
Autor:
Novack, Zachary, Zhu, Ge, Casebeer, Jonah, McAuley, Julian, Berg-Kirkpatrick, Taylor, Bryan, Nicholas J.
Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling ste
Externí odkaz:
http://arxiv.org/abs/2410.05167
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