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 learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.

Position: Topological Deep Learning is the New Frontier for Relational Learning / Papamarkou, T.; Birdal, T.; Bronstein, M.; Carlsson, G.; Curry, J.; Gao, Y.; Hajij, M.; Kwitt, R.; Lio, P.; Di Lorenzo, P.; Maroulas, V.; Miolane, N.; Nasrin, F.; Ramamurthy, K. N.; Rieck, B.; Scardapane, S.; Schaub, M. T.; Velickovic, P.; Wang, B.; Wang, Y.; Wei, G. -W.; Zamzmi, G.. - 235:(2024), pp. 39529-39555. (Intervento presentato al convegno 41st International Conference on Machine Learning, ICML 2024 tenutosi a Vienna; aut).

Position: Topological Deep Learning is the New Frontier for Relational Learning

Lio P.;Scardapane S.;
2024

Abstract

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 learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.
2024
41st International Conference on Machine Learning, ICML 2024
Contrastive Learning; Deep reinforcement learning; Federated learning; Topology
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Position: Topological Deep Learning is the New Frontier for Relational Learning / Papamarkou, T.; Birdal, T.; Bronstein, M.; Carlsson, G.; Curry, J.; Gao, Y.; Hajij, M.; Kwitt, R.; Lio, P.; Di Lorenzo, P.; Maroulas, V.; Miolane, N.; Nasrin, F.; Ramamurthy, K. N.; Rieck, B.; Scardapane, S.; Schaub, M. T.; Velickovic, P.; Wang, B.; Wang, Y.; Wei, G. -W.; Zamzmi, G.. - 235:(2024), pp. 39529-39555. (Intervento presentato al convegno 41st International Conference on Machine Learning, ICML 2024 tenutosi a Vienna; aut).
File allegati a questo prodotto
File Dimensione Formato  
Papamarkou_Position_2024.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 493.82 kB
Formato Adobe PDF
493.82 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1728695
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact