Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Papamarkou, Theodore"'
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:
Papamarkou, Theodore, Birdal, Tolga, Bronstein, Michael, Carlsson, Gunnar, Curry, Justin, Gao, Yue, Hajij, Mustafa, Kwitt, Roland, Liò, Pietro, Di Lorenzo, Paolo, Maroulas, Vasileios, Miolane, Nina, Nasrin, Farzana, Ramamurthy, Karthikeyan Natesan, Rieck, Bastian, Scardapane, Simone, Schaub, Michael T., Veličković, Petar, Wang, Bei, Wang, Yusu, Wei, Guo-Wei, Zamzmi, Ghada
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation
Externí odkaz:
http://arxiv.org/abs/2402.08871
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:
Sommer, Emanuel, Wimmer, Lisa, Papamarkou, Theodore, Bothmann, Ludwig, Bischl, Bernd, Rügamer, David
A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size and structure of the networks' parameter space. Our work shows that successful SBI is possible by embracing the characteristic relationship between weight and
Externí odkaz:
http://arxiv.org/abs/2402.01484
Autor:
Papamarkou, Theodore, Skoularidou, Maria, Palla, Konstantina, Aitchison, Laurence, Arbel, Julyan, Dunson, David, Filippone, Maurizio, Fortuin, Vincent, Hennig, Philipp, Hernández-Lobato, José Miguel, Hubin, Aliaksandr, Immer, Alexander, Karaletsos, Theofanis, Khan, Mohammad Emtiyaz, Kristiadi, Agustinus, Li, Yingzhen, Mandt, Stephan, Nemeth, Christopher, Osborne, Michael A., Rudner, Tim G. J., Rügamer, David, Teh, Yee Whye, Welling, Max, Wilson, Andrew Gordon, Zhang, Ruqi
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooke
Externí odkaz:
http://arxiv.org/abs/2402.00809
Autor:
Hajij, Mustafa, Zamzmi, Ghada, Papamarkou, Theodore, Guzmán-Sáenz, Aldo, Birdal, Tolga, Schaub, Michael T.
Publikováno v:
57th Asilomar Conference on Signals, Systems, and Computers, 2023
Graph-based signal processing techniques have become essential for handling data in non-Euclidean spaces. However, there is a growing awareness that these graph models might need to be expanded into `higher-order' domains to effectively represent the
Externí odkaz:
http://arxiv.org/abs/2312.09504
In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the r
Externí odkaz:
http://arxiv.org/abs/2310.12842
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:
Wiese, Jonas Gregor, Wimmer, Lisa, Papamarkou, Theodore, Bischl, Bernd, Günnemann, Stephan, Rügamer, David
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are considered prohib
Externí odkaz:
http://arxiv.org/abs/2304.02902
Autor:
Papamarkou, Theodore
In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of parameters via a blocked Gibbs sampling scheme. By partitioning the parameter space, sampling is possible
Externí odkaz:
http://arxiv.org/abs/2208.11389