Zobrazeno 1 - 10
of 873
pro vyhledávání: '"Hu,Weihua"'
Autor:
Yuan, Yiwen, Zhang, Zecheng, He, Xinwei, Nitta, Akihiro, Hu, Weihua, Wang, Dong, Shah, Manan, Huang, Shenyang, Stojanovič, Blaž, Krumholz, Alan, Lenssen, Jan Eric, Leskovec, Jure, Fey, Matthias
Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic repre
Externí odkaz:
http://arxiv.org/abs/2411.19513
Autor:
Robinson, Joshua, Ranjan, Rishabh, Hu, Weihua, Huang, Kexin, Han, Jiaqi, Dobles, Alejandro, Fey, Matthias, Lenssen, Jan E., Yuan, Yiwen, Zhang, Zecheng, He, Xinwei, Leskovec, Jure
We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse domains and scales, and is intended to be a foundational infrastructure
Externí odkaz:
http://arxiv.org/abs/2407.20060
Autor:
Hu, Weihua, Yuan, Yiwen, Zhang, Zecheng, Nitta, Akihiro, Cao, Kaidi, Kocijan, Vid, Sunil, Jinu, Leskovec, Jure, Fey, Matthias
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular data, introducing a model abstra
Externí odkaz:
http://arxiv.org/abs/2404.00776
Autor:
Chen, Jialin, Lenssen, Jan Eric, Feng, Aosong, Hu, Weihua, Fey, Matthias, Tassiulas, Leandros, Leskovec, Jure, Ying, Rex
Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting performance by treating different channels individually, while it leads
Externí odkaz:
http://arxiv.org/abs/2404.01340
Autor:
Fey, Matthias, Hu, Weihua, Huang, Kexin, Lenssen, Jan Eric, Ranjan, Rishabh, Robinson, Joshua, Ying, Rex, You, Jiaxuan, Leskovec, Jure
Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations. However, building machine learning models using this data is both cha
Externí odkaz:
http://arxiv.org/abs/2312.04615
Autor:
Huang, Shenyang, Poursafaei, Farimah, Danovitch, Jacob, Fey, Matthias, Hu, Weihua, Rossi, Emanuele, Leskovec, Jure, Bronstein, Michael, Rabusseau, Guillaume, Rabbany, Reihaneh
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning year
Externí odkaz:
http://arxiv.org/abs/2307.01026
Autor:
Lam, Remi, Sanchez-Gonzalez, Alvaro, Willson, Matthew, Wirnsberger, Peter, Fortunato, Meire, Alet, Ferran, Ravuri, Suman, Ewalds, Timo, Eaton-Rosen, Zach, Hu, Weihua, Merose, Alexander, Hoyer, Stephan, Holland, George, Vinyals, Oriol, Stott, Jacklynn, Pritzel, Alexander, Mohamed, Shakir, Battaglia, Peter
Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but cannot directly use historical
Externí odkaz:
http://arxiv.org/abs/2212.12794
Autor:
Hu, Weihua, Cao, Kaidi, Huang, Kexin, Huang, Edward W, Subbian, Karthik, Kawaguchi, Kenji, Leskovec, Jure
Despite recent advances in Graph Neural Networks (GNNs), their training strategies remain largely under-explored. The conventional training strategy learns over all nodes in the original graph(s) equally, which can be sub-optimal as certain nodes are
Externí odkaz:
http://arxiv.org/abs/2210.14843
Embeddings, low-dimensional vector representation of objects, are fundamental in building modern machine learning systems. In industrial settings, there is usually an embedding team that trains an embedding model to solve intended tasks (e.g., produc
Externí odkaz:
http://arxiv.org/abs/2206.03040
Publikováno v:
In Applied Materials Today February 2025 42
