Zobrazeno 1 - 10
of 24 008
pro vyhledávání: '"An, Tianqi"'
We present the MIDInfinite, a web application capable of generating symbolic music using a large-scale generative AI model locally on commodity hardware. Creating this demo involved porting the Anticipatory Music Transformer, a large language model (
Externí odkaz:
http://arxiv.org/abs/2411.09625
In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP leverages the zero-shot detection capabilities of vision-language found
Externí odkaz:
http://arxiv.org/abs/2411.06702
Autor:
Wang, Tianqi, Zimmer, Andrew
In this paper we investigate the Gromov hyperbolicity of the classical Kobayashi and Hilbert metrics, and the recently introduced minimal metric. Using the linear isoperimetric inequality characterization of Gromov hyperbolicity, we show if these met
Externí odkaz:
http://arxiv.org/abs/2411.06579
Multi-agent systems - systems with multiple independent AI agents working together to achieve a common goal - are becoming increasingly prevalent in daily life. Drawing inspiration from the phenomenon of human group social influence, we investigate w
Externí odkaz:
http://arxiv.org/abs/2411.04578
Autor:
Ye, Ziyu, Agarwal, Rishabh, Liu, Tianqi, Joshi, Rishabh, Velury, Sarmishta, Le, Quoc V., Tan, Qijun, Liu, Yuan
Current RLHF frameworks for aligning large language models (LLMs) typically assume a fixed prompt distribution, which is sub-optimal and limits the scalability of alignment and generalizability of models. To address this, we introduce a general open-
Externí odkaz:
http://arxiv.org/abs/2411.00062
Quantum secret sharing (QSS) plays a pivotal role in multiparty quantum communication, ensuring the secure distribution of private information among multiple parties. However, the security of QSS schemes can be compromised by attacks exploiting imper
Externí odkaz:
http://arxiv.org/abs/2410.23562
Autor:
Chen, Junting, Yu, Checheng, Zhou, Xunzhe, Xu, Tianqi, Mu, Yao, Hu, Mengkang, Shao, Wenqi, Wang, Yikai, Li, Guohao, Shao, Lin
Heterogeneous multi-robot systems (HMRS) have emerged as a powerful approach for tackling complex tasks that single robots cannot manage alone. Current large-language-model-based multi-agent systems (LLM-based MAS) have shown success in areas like so
Externí odkaz:
http://arxiv.org/abs/2410.22662
Query Autocomplete (QAC) is a critical feature in modern search engines, facilitating user interaction by predicting search queries based on input prefixes. Despite its widespread adoption, the absence of large-scale, realistic datasets has hindered
Externí odkaz:
http://arxiv.org/abs/2411.04129
Autor:
Chi, Yizhou, Lin, Yizhang, Hong, Sirui, Pan, Duyi, Fei, Yaying, Mei, Guanghao, Liu, Bangbang, Pang, Tianqi, Kwok, Jacky, Zhang, Ceyao, Liu, Bang, Wu, Chenglin
Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines. While LLM-based agents have shown
Externí odkaz:
http://arxiv.org/abs/2410.17238
Probabilistic Bit (P-Bit) device serves as the core hardware for implementing Ising computation. However, the severe intrinsic variations of stochastic P-Bit devices hinder the large-scale expansion of the P-Bit array, significantly limiting the prac
Externí odkaz:
http://arxiv.org/abs/2410.16915