Zobrazeno 1 - 10
of 28 585
pro vyhledávání: '"WEI, Yang"'
In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. Recently, we have witnessed the rise of variational autoencoder-based latent factor models, which learn latent factors self-adaptively. Wh
Externí odkaz:
http://arxiv.org/abs/2412.09468
Autor:
Zhang, Wei-Yang, Dong, Feng-Lian, Wei, Zhi-Wei, Wang, Yan-Ru, Xu, Ze-Jin, Chen, Wei-Kun, Dai, Yu-Hong
The distributed recursion (DR) algorithm is an effective method for solving the pooling problem that arises in many applications. It is based on the well-known P-formulation of the pooling problem, which involves the flow and quality variables; and i
Externí odkaz:
http://arxiv.org/abs/2411.09554
Autor:
Yu, Wei-Yang, Joseph, V. Roshan
In this work, we propose an automatic method for the analysis of experiments that incorporates hierarchical relationships between the experimental variables. We use a modified version of nonnegative garrote method for variable selection which can inc
Externí odkaz:
http://arxiv.org/abs/2411.01383
Autor:
Yang, Hanlin, Yao, Jian, Liu, Weiming, Wang, Qing, Qin, Hanmin, Kong, Hansheng, Tang, Kirk, Xiong, Jiechao, Yu, Chao, Li, Kai, Xing, Junliang, Chen, Hongwu, Zhuo, Juchao, Fu, Qiang, Wei, Yang, Fu, Haobo
Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory, previous diverse policies recovering methods usually employ a vanilla
Externí odkaz:
http://arxiv.org/abs/2410.15910
Federated Domain Adaptation (FDA) is a Federated Learning (FL) scenario where models are trained across multiple clients with unique data domains but a shared category space, without transmitting private data. The primary challenge in FDA is data het
Externí odkaz:
http://arxiv.org/abs/2410.07738
We present a multi-frequency polarimetric study for the quasar 1604+159. The source was observed at the $L$ band with the American Very Long Baseline Array (VLBA) and the $L$, $X$, and $U$ bands with the Very Large Array (VLA). These observations pro
Externí odkaz:
http://arxiv.org/abs/2408.06647
Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. Ho
Externí odkaz:
http://arxiv.org/abs/2407.04029
Autor:
Yu, Zijie, Deng, Furen, Sun, Shijie, Niu, Chenhui, Li, Jixia, Wu, Fengquan, Wang, Wei-Yang, Wang, Yougang, Zuo, Shifan, Shu, Lin, Hao, Jie, Liu, Xiaohui, Ansari, Reza, Pen, Ue-Li, Stebbins, Albert, Timbie, Peter, Chen, Xuelei
Publikováno v:
Research in Astronomy and Astrophysics, 24, id.085010 (2024)
This paper presents the design, calibration, and survey strategy of the Fast Radio Burst (FRB) digital backend and its real-time data processing pipeline employed in the Tianlai Cylinder Pathfinder array. The array, consisting of three parallel cylin
Externí odkaz:
http://arxiv.org/abs/2406.15740
In the paper, we consider the competitive facility location problem with limited choice rule (CFLPLCR), which attempts to open a subset of facilities to maximize the net profit of a newcomer company, requiring customers to patronize only a limited nu
Externí odkaz:
http://arxiv.org/abs/2406.05775
Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness
Externí odkaz:
http://arxiv.org/abs/2405.18802