Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Bernárdez, Guillermo"'
Autor:
Bernárdez, Guillermo, Telyatnikov, Lev, Montagna, Marco, Baccini, Federica, Papillon, Mathilde, Ferriol-Galmés, Miquel, Hajij, Mustafa, Papamarkou, Theodore, Bucarelli, Maria Sofia, Zaghen, Olga, Mathe, Johan, Myers, Audun, Mahan, Scott, Lillemark, Hansen, Vadgama, Sharvaree, Bekkers, Erik, Doster, Tim, Emerson, Tegan, Kvinge, Henry, Agate, Katrina, Ahmed, Nesreen K, Bai, Pengfei, Banf, Michael, Battiloro, Claudio, Beketov, Maxim, Bogdan, Paul, Carrasco, Martin, Cavallo, Andrea, Choi, Yun Young, Dasoulas, George, Elphick, Matouš, Escalona, Giordan, Filipiak, Dominik, Fritze, Halley, Gebhart, Thomas, Gil-Sorribes, Manel, Goomanee, Salvish, Guallar, Victor, Imasheva, Liliya, Irimia, Andrei, Jin, Hongwei, Johnson, Graham, Kanakaris, Nikos, Koloski, Boshko, Kovač, Veljko, Lecha, Manuel, Lee, Minho, Leroy, Pierrick, Long, Theodore, Magai, German, Martinez, Alvaro, Masden, Marissa, Mežnar, Sebastian, Miquel-Oliver, Bertran, Molina, Alexis, Nikitin, Alexander, Nurisso, Marco, Piekenbrock, Matt, Qin, Yu, Rygiel, Patryk, Salatiello, Alessandro, Schattauer, Max, Snopov, Pavel, Suk, Julian, Sánchez, Valentina, Tec, Mauricio, Vaccarino, Francesco, Verhellen, Jonas, Wantiez, Frederic, Weers, Alexander, Zajec, Patrik, Škrlj, Blaž, Miolane, Nina
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem
Externí odkaz:
http://arxiv.org/abs/2409.05211
Autor:
Telyatnikov, Lev, Bernardez, Guillermo, Montagna, Marco, Vasylenko, Pavlo, Zamzmi, Ghada, Hajij, Mustafa, Schaub, Michael T, Miolane, Nina, Scardapane, Simone, Papamarkou, Theodore
This work introduces TopoBenchmarkX, a modular open-source library designed to standardize benchmarking and accelerate research in Topological Deep Learning (TDL). TopoBenchmarkX maps the TDL pipeline into a sequence of independent and modular compon
Externí odkaz:
http://arxiv.org/abs/2406.06642
Autor:
Hajij, Mustafa, Papillon, Mathilde, Frantzen, Florian, Agerberg, Jens, AlJabea, Ibrahem, Ballester, Ruben, Battiloro, Claudio, Bernárdez, Guillermo, Birdal, Tolga, Brent, Aiden, Chin, Peter, Escalera, Sergio, Fiorellino, Simone, Gardaa, Odin Hoff, Gopalakrishnan, Gurusankar, Govil, Devendra, Hoppe, Josef, Karri, Maneel Reddy, Khouja, Jude, Lecha, Manuel, Livesay, Neal, Meißner, Jan, Mukherjee, Soham, Nikitin, Alexander, Papamarkou, Theodore, Prílepok, Jaro, Ramamurthy, Karthikeyan Natesan, Rosen, Paul, Guzmán-Sáenz, Aldo, Salatiello, Alessandro, Samaga, Shreyas N., Scardapane, Simone, Schaub, Michael T., Scofano, Luca, Spinelli, Indro, Telyatnikov, Lev, Truong, Quang, Walters, Robin, Yang, Maosheng, Zaghen, Olga, Zamzmi, Ghada, Zia, Ali, Miolane, Nina
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. To
Externí odkaz:
http://arxiv.org/abs/2402.02441
Autor:
Telyatnikov, Lev, Bucarelli, Maria Sofia, Bernardez, Guillermo, Zaghen, Olga, Scardapane, Simone, Lio, Pietro
Most of the current hypergraph learning methodologies and benchmarking datasets in the hypergraph realm are obtained by lifting procedures from their graph analogs, leading to overshadowing specific characteristics of hypergraphs. This paper attempts
Externí odkaz:
http://arxiv.org/abs/2310.07684
Autor:
Papillon, Mathilde, Hajij, Mustafa, Jenne, Helen, Mathe, Johan, Myers, Audun, Papamarkou, Theodore, Birdal, Tolga, Dey, Tamal, Doster, Tim, Emerson, Tegan, Gopalakrishnan, Gurusankar, Govil, Devendra, Guzmán-Sáenz, Aldo, Kvinge, Henry, Livesay, Neal, Mukherjee, Soham, Samaga, Shreyas N., Ramamurthy, Karthikeyan Natesan, Karri, Maneel Reddy, Rosen, Paul, Sanborn, Sophia, Walters, Robin, Agerberg, Jens, Barikbin, Sadrodin, Battiloro, Claudio, Bazhenov, Gleb, Bernardez, Guillermo, Brent, Aiden, Escalera, Sergio, Fiorellino, Simone, Gavrilev, Dmitrii, Hassanin, Mohammed, Häusner, Paul, Gardaa, Odin Hoff, Khamis, Abdelwahed, Lecha, Manuel, Magai, German, Malygina, Tatiana, Ballester, Rubén, Nadimpalli, Kalyan, Nikitin, Alexander, Rabinowitz, Abraham, Salatiello, Alessandro, Scardapane, Simone, Scofano, Luca, Singh, Suraj, Sjölund, Jens, Snopov, Pavel, Spinelli, Indro, Telyatnikov, Lev, Testa, Lucia, Yang, Maosheng, Yue, Yixiao, Zaghen, Olga, Zia, Ali, Miolane, Nina
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topo
Externí odkaz:
http://arxiv.org/abs/2309.15188
Autor:
Bernárdez, Guillermo, Telyatnikov, Lev, Alarcón, Eduard, Cabellos-Aparicio, Albert, Barlet-Ros, Pere, Liò, Pietro
Recently emerged Topological Deep Learning (TDL) methods aim to extend current Graph Neural Networks (GNN) by naturally processing higher-order interactions, going beyond the pairwise relations and local neighborhoods defined by graph representations
Externí odkaz:
http://arxiv.org/abs/2308.11068
Autor:
Bernárdez, Guillermo, Suárez-Varela, José, Shi, Xiang, Xiao, Shihan, Cheng, Xiangle, Barlet-Ros, Pere, Cabellos-Aparicio, Albert
Congestion Control (CC) plays a fundamental role in optimizing traffic in Data Center Networks (DCN). Currently, DCNs mainly implement two main CC protocols: DCTCP and DCQCN. Both protocols -- and their main variants -- are based on Explicit Congesti
Externí odkaz:
http://arxiv.org/abs/2308.04905
Autor:
Bernárdez, Guillermo, Suárez-Varela, José, López, Albert, Shi, Xiang, Xiao, Shihan, Cheng, Xiangle, Barlet-Ros, Pere, Cabellos-Aparicio, Albert
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based solutions for Traffic Engineering (TE), which is a fundamental problem in
Externí odkaz:
http://arxiv.org/abs/2303.18157
Optimal Power Flow (OPF) is a very traditional research area within the power systems field that seeks for the optimal operation point of electric power plants, and which needs to be solved every few minutes in real-world scenarios. However, due to t
Externí odkaz:
http://arxiv.org/abs/2212.12470
Autor:
Bernárdez, Guillermo, Suárez-Varela, José, López, Albert, Wu, Bo, Xiao, Shihan, Cheng, Xiangle, Barlet-Ros, Pere, Cabellos-Aparicio, Albert
Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between
Externí odkaz:
http://arxiv.org/abs/2109.01445