Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Huang Shenyang"'
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:
Huang, Shenyang, Yu, Boyang, Ma, Yixuan, Pan, Chenghao, Ma, Junwei, Zhou, Yuxuan, Ma, Yaozhenghang, Yang, Ke, Wu, Hua, Lei, Yuchen, Xing, Qiaoxia, Mu, Lei, Zhang, Jiasheng, Mou, Yanlin, Yan, Hugen
Publikováno v:
Science386,526-531(2024)
Bright dipolar excitons, which contain electrical dipoles and have high oscillator strength, are an ideal platform for studying correlated quantum phenomena. They usually rely on carrier tunneling between two quantum wells or two layers to hybridize
Externí odkaz:
http://arxiv.org/abs/2411.01905
Autor:
Huang, Shenyang, Poursafaei, Farimah, Rabbany, Reihaneh, Rabusseau, Guillaume, Rossi, Emanuele
Many real world graphs are inherently dynamic, constantly evolving with node and edge additions. These graphs can be represented by temporal graphs, either through a stream of edge events or a sequence of graph snapshots. Until now, the development o
Externí odkaz:
http://arxiv.org/abs/2407.12269
Autor:
Shirzadkhani, Razieh, Ngo, Tran Gia Bao, Shamsi, Kiarash, Huang, Shenyang, Poursafaei, Farimah, Azad, Poupak, Rabbany, Reihaneh, Coskunuzer, Baris, Rabusseau, Guillaume, Akcora, Cuneyt Gurcan
The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observed temporal graphs, is it possible to predict the evolution of an unseen network from the same domain? To answe
Externí odkaz:
http://arxiv.org/abs/2406.10426
Autor:
Gastinger, Julia, Huang, Shenyang, Galkin, Mikhail, Loghmani, Erfan, Parviz, Ali, Poursafaei, Farimah, Danovitch, Jacob, Rossi, Emanuele, Koutis, Ioannis, Stuckenschmidt, Heiner, Rabbany, Reihaneh, Rabusseau, Guillaume
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust
Externí odkaz:
http://arxiv.org/abs/2406.09639
Evolving relations in real-world networks are often modelled by temporal graphs. Temporal Graph Neural Networks (TGNNs) emerged to model evolutionary behaviour of such graphs by leveraging the message passing primitive at the core of Graph Neural Net
Externí odkaz:
http://arxiv.org/abs/2406.02362
Autor:
Kläser, Kerstin, Banaszewski, Błażej, Maddrell-Mander, Samuel, McLean, Callum, Müller, Luis, Parviz, Ali, Huang, Shenyang, Fitzgibbon, Andrew
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data and then transfer to low-data downstream tasks is a promising direction
Externí odkaz:
http://arxiv.org/abs/2404.14986
Autor:
Shirzadkhani, Razieh, Huang, Shenyang, Kooshafar, Elahe, Rabbany, Reihaneh, Poursafaei, Farimah
Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely designed for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introd
Externí odkaz:
http://arxiv.org/abs/2402.03651
Social media platforms such as Twitter (now known as X) have revolutionized how the public engage with important societal and political topics. Recently, climate change discussions on social media became a catalyst for political polarization and the
Externí odkaz:
http://arxiv.org/abs/2312.01217
Autor:
Liu, Yaxin, Zhu, Bingbing, Jiang, Shicheng, Huang, Shenyang, Luo, Mingyan, Zhang, Sheng, Yan, Hugen, Zhang, Yuanbo, Lu, Ruifeng, Tao, Zhensheng
Floquet engineering, while a powerful tool for ultrafast quantum-state manipulation, faces challenges under strong-field conditions, as recent high harmonic generation studies unveil exceptionally short dephasing times. In this study, using time- and
Externí odkaz:
http://arxiv.org/abs/2311.10286