Zobrazeno 1 - 10
of 72
pro vyhledávání: '"TU, Weiwei"'
Reinforcement learning has achieved great success in many decision-making tasks, and traditional reinforcement learning algorithms are mainly designed for obtaining a single optimal solution. However, recent works show the importance of developing di
Externí odkaz:
http://arxiv.org/abs/2308.11924
Molecular property prediction is an important problem in drug discovery and materials science. As geometric structures have been demonstrated necessary for molecular property prediction, 3D information has been combined with various graph learning me
Externí odkaz:
http://arxiv.org/abs/2306.07812
Double-strand DNA breaks (DSBs) are a form of DNA damage that can cause abnormal chromosomal rearrangements. Recent technologies based on high-throughput experiments have obvious high costs and technical challenges.Therefore, we design a graph neural
Externí odkaz:
http://arxiv.org/abs/2201.01855
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing works mainly focus on feature interactions and ignore sample relations,
Externí odkaz:
http://arxiv.org/abs/2108.09127
Recent years have witnessed the popularity and success of graph neural networks (GNN) in various scenarios. To obtain data-specific GNN architectures, researchers turn to neural architecture search (NAS), which has made impressive success in discover
Externí odkaz:
http://arxiv.org/abs/2104.06608
Tabular data is the most common data format adopted by our customers ranging from retail, finance to E-commerce, and tabular data classification plays an essential role to their businesses. In this paper, we present Network On Network (NON), a practi
Externí odkaz:
http://arxiv.org/abs/2005.10114
Autor:
Tu, Weiwei, Dong, Jiuxiang
Publikováno v:
In Journal of the Franklin Institute September 2023 360(13):10227-10250
The Adam algorithm has become extremely popular for large-scale machine learning. Under convexity condition, it has been proved to enjoy a data-dependant $O(\sqrt{T})$ regret bound where $T$ is the time horizon. However, whether strong convexity can
Externí odkaz:
http://arxiv.org/abs/1905.02957
Autor:
Luo, Yuanfei, Wang, Mengshuo, Zhou, Hao, Yao, Quanming, Tu, WeiWei, Chen, Yuqiang, Yang, Qiang, Dai, Wenyuan
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its custo
Externí odkaz:
http://arxiv.org/abs/1904.12857