Zobrazeno 1 - 10
of 228
pro vyhledávání: '"Huang, Shenyang"'
Autor:
Huang, Shenyang, Poursafaei, Farimah, Rabbany, Reihaneh, Rabusseau, Guillaume, Rossi, Emanuele
Temporal graphs have gained increasing importance due to their ability to model dynamically evolving relationships. These graphs can be represented through either a stream of edge events or a sequence of graph snapshots. Until now, the development of
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
Autor:
Beaini, Dominique, Huang, Shenyang, Cunha, Joao Alex, Li, Zhiyi, Moisescu-Pareja, Gabriela, Dymov, Oleksandr, Maddrell-Mander, Samuel, McLean, Callum, Wenkel, Frederik, Müller, Luis, Mohamud, Jama Hussein, Parviz, Ali, Craig, Michael, Koziarski, Michał, Lu, Jiarui, Zhu, Zhaocheng, Gabellini, Cristian, Klaser, Kerstin, Dean, Josef, Wognum, Cas, Sypetkowski, Maciej, Rabusseau, Guillaume, Rabbany, Reihaneh, Tang, Jian, Morris, Christopher, Koutis, Ioannis, Ravanelli, Mirco, Wolf, Guy, Tossou, Prudencio, Mary, Hadrien, Bois, Therence, Fitzgibbon, Andrew, Banaszewski, Błażej, Martin, Chad, Masters, Dominic
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, an
Externí odkaz:
http://arxiv.org/abs/2310.04292
Autor:
Lei, Yuchen, Ma, Junwei, Luo, Jiaming, Huang, Shenyang, Yu, Boyang, Song, Chaoyu, Xing, Qiaoxia, Wang, Fanjie, Xie, Yuangang, Zhang, Jiasheng, Mu, Lei, Ma, Yixuan, Wang, Chong, Yan, Hugen
The evolution of excitons from 2D to 3D is of great importance in photo-physics, yet the layer-dependent exciton polarizability has not been investigated in 2D semiconductors. Here, we determine the exciton polarizabilities for 3- to 11-layer black p
Externí odkaz:
http://arxiv.org/abs/2309.10327