Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Rabusseau, Guillaume"'
Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical methods. However, current quantum algorithms are constrained by hardw
Externí odkaz:
http://arxiv.org/abs/2410.16041
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
Model training requires significantly more memory, compared with inference. Parameter efficient fine-tuning (PEFT) methods provide a means of adapting large models to downstream tasks using less memory. However, existing methods such as adapters, pro
Externí odkaz:
http://arxiv.org/abs/2407.07802
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
Second-order Recurrent Neural Networks (2RNNs) extend RNNs by leveraging second-order interactions for sequence modelling. These models are provably more expressive than their first-order counterparts and have connections to well-studied models from
Externí odkaz:
http://arxiv.org/abs/2406.05045
Tensor Train~(TT) decomposition is widely used in the machine learning and quantum physics communities as a popular tool to efficiently compress high-dimensional tensor data. In this paper, we propose an efficient algorithm to accelerate computing th
Externí odkaz:
http://arxiv.org/abs/2406.02749
Transformers are ubiquitous models in the natural language processing (NLP) community and have shown impressive empirical successes in the past few years. However, little is understood about how they reason and the limits of their computational capab
Externí odkaz:
http://arxiv.org/abs/2403.09728
Autor:
Meiburg, Alex, Chen, Jing, Miller, Jacob, Tihon, Raphaëlle, Rabusseau, Guillaume, Perdomo-Ortiz, Alejandro
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning. While possessing many desirable features arisin
Externí odkaz:
http://arxiv.org/abs/2310.20498
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