Zobrazeno 1 - 10
of 49 581
pro vyhledávání: '"Liu, Xin A."'
Autor:
Liu, Rui-Heng, Liu, Xin
Flat band (FB) systems provide ideal playgrounds for studying correlation physics, whereas multi-orbital characteristics in real materials are distinguished from most simple FB models. Here, we propose a systematic and versatile framework for FB cons
Externí odkaz:
http://arxiv.org/abs/2412.15653
Split Federated Learning (SFL) is a distributed machine learning paradigm that combines federated learning and split learning. In SFL, a neural network is partitioned at a cut layer, with the initial layers deployed on clients and remaining layers on
Externí odkaz:
http://arxiv.org/abs/2412.15536
We investigate the QCD phase transition and its phase structure within Einstein-Maxwell-Dilaton-scalar system and compare the results with those obtained from the Einstein-Maxwell-Dilaton system. It is shown that both models reproduce behavior consis
Externí odkaz:
http://arxiv.org/abs/2412.15149
Editing videos with textual guidance has garnered popularity due to its streamlined process which mandates users to solely edit the text prompt corresponding to the source video. Recent studies have explored and exploited large-scale text-to-image di
Externí odkaz:
http://arxiv.org/abs/2412.11710
Autor:
Bai, Jiaxin, Wang, Zhaobo, Cheng, Junfei, Yu, Dan, Huang, Zerui, Wang, Weiqi, Liu, Xin, Luo, Chen, He, Qi, Zhu, Yanming, Li, Bo, Song, Yangqiu
Understanding user intentions is challenging for online platforms. Recent work on intention knowledge graphs addresses this but often lacks focus on connecting intentions, which is crucial for modeling user behavior and predicting future actions. Thi
Externí odkaz:
http://arxiv.org/abs/2412.11500
Autor:
Liu, Xin, Chen, Yaran
Current advanced policy learning methodologies have demonstrated the ability to develop expert-level strategies when provided enough information. However, their requirements, including task-specific rewards, expert-labeled trajectories, and huge envi
Externí odkaz:
http://arxiv.org/abs/2412.10778
Ensembles of generative large language models (LLMs) can integrate the strengths of different LLMs to compensate for the limitations of individual models. However, recent work has focused on training an additional fusion model to combine complete res
Externí odkaz:
http://arxiv.org/abs/2412.07380
This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effecti
Externí odkaz:
http://arxiv.org/abs/2412.06075
Autor:
Liu, Shanqi, Liu, Xin
This paper studies online convex optimization with unknown linear budget constraints, where only the gradient information of the objective and the bandit feedback of constraint functions are observed. We propose a safe and efficient Lyapunov-optimiza
Externí odkaz:
http://arxiv.org/abs/2412.03983
Autor:
Merz, Grant, Liu, Xin, Schmidt, Samuel, Malz, Alex I., Zhang, Tianqing, Branton, Doug, Burke, Colin J., Delucchi, Melissa, Ejjagiri, Yaswant Sai, Kubica, Jeremy, Liu, Yichen, Lynn, Olivia, Oldag, Drew, Collaboration, The LSST Dark Energy Science
Photometric redshifts will be a key data product for the Rubin Observatory Legacy Survey of Space and Time (LSST) as well as for future ground and space-based surveys. The need for photometric redshifts, or photo-zs, arises from sparse spectroscopic
Externí odkaz:
http://arxiv.org/abs/2411.18769