Zobrazeno 1 - 10
of 2 469
pro vyhledávání: '"Wang, TianQi"'
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
Autor:
Wang, Tianqi, Singh, Shubham
Inspired by recent advances in Kolmogorov-Arnold Networks (KANs), we introduce a novel approach to latent factor conditional asset pricing models. While previous machine learning applications in asset pricing have predominantly used Multilayer Percep
Externí odkaz:
http://arxiv.org/abs/2408.02694
Class-incremental learning (CIL) aims to train a model to learn new classes from non-stationary data streams without forgetting old ones. In this paper, we propose a new kind of connectionist model by tailoring neural unit dynamics that adapt the beh
Externí odkaz:
http://arxiv.org/abs/2406.02428
End-to-end driving has made significant progress in recent years, demonstrating benefits such as system simplicity and competitive driving performance under both open-loop and closed-loop settings. Nevertheless, the lack of interpretability and contr
Externí odkaz:
http://arxiv.org/abs/2403.16996
Autor:
Wang, Ruofan, Prabhakar, Prakruthi, Srivastava, Gaurav, Wang, Tianqi, Jalali, Zeinab S., Bharill, Varun, Ouyang, Yunbo, Nigam, Aastha, Venugopalan, Divya, Gupta, Aman, Borisyuk, Fedor, Keerthi, Sathiya, Muralidharan, Ajith
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model up
Externí odkaz:
http://arxiv.org/abs/2403.00803
Autor:
Wang, Lening, Ren, Yilong, Jiang, Han, Cai, Pinlong, Fu, Daocheng, Wang, Tianqi, Cui, Zhiyong, Yu, Haiyang, Wang, Xuesong, Zhou, Hanchu, Huang, Helai, Wang, Yinhai
Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic safety. However, previous studies, whether focusing on static environm
Externí odkaz:
http://arxiv.org/abs/2312.13156
Autor:
Wang, Tianqi
We prove that divergent, extended geometrically finite (in the sense of Weisman arXiv:2205.07183) representations can be interpreted as restricted Anosov (in the sense of Tholozan--Wang arXiv:2307.02934) representations over certain flow spaces. We a
Externí odkaz:
http://arxiv.org/abs/2309.15636
Autor:
Guo, Jingwen, Zhou, Liqin, Ding, Jianyang, Qu, Gexing, Liu, Zhengtai, Du, Yu, Zhang, Heng, Li, Jiajun, Zhang, Yiying, Zhou, Fuwei, Qi, Wuyi, Guo, Fengyi, Wang, Tianqi, Fei, Fucong, Huang, Yaobo, Qian, Tian, Shen, Dawei, Weng, Hongming, Song, Fengqi
Publikováno v:
Science Bulletin 06.36.2024
Kagome materials have attracted a surge of research interest recently, especially for the ones combining with magnetism, and the ones with weak interlayer interactions which can fabricate thin devices. However, kagome materials combining both charact
Externí odkaz:
http://arxiv.org/abs/2308.14509
Autor:
Tholozan, Nicolas, Wang, Tianqi
We introduce and study \emph{simple Anosov representations} of closed hyperbolic surface groups, analogous to Minsky's \emph{primitive stable representations} of free groups. We prove that the set of simple Anosov representations into $\mathrm{SL}(d,
Externí odkaz:
http://arxiv.org/abs/2307.02934
Autor:
Li, Depeng, Wang, Tianqi, Xu, Bingrong, Kawaguchi, Kenji, Zeng, Zhigang, Suganthan, Ponnuthurai Nagaratnam
Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which information is stored in weights on connections, we investigated how to desi
Externí odkaz:
http://arxiv.org/abs/2306.10480