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

• URL: http://arxiv.org/abs/2402.08871v3
• Date: Tue, 6 Aug 2024 16:38:41 GMT
• Title: Position: Topological Deep Learning is the New Frontier for Relational Learning
• Authors: Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Veličković, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi,
• Abstract summary: 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.
• Score: 51.05869778335334
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models [48.559185522099625]
  Planning is a crucial element of both human intelligence and contemporary large language models (LLMs) This paper investigates the emergence of planning capabilities in Transformer-based LLMs via their next-word prediction mechanisms.
  arXiv  Detail & Related papers  (2024-05-15T09:59:37Z)
• Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions [10.753191494611892]
  We dive into Tabular Data Learning using Graph Neural Networks (GNNs) GNNs have garnered significant interest and application across various Tabular Data Learning domains. This survey serves as a resource for researchers and practitioners, offering a thorough understanding of GNNs' role in revolutionizing TDL.
  arXiv  Detail & Related papers  (2024-01-04T08:49:10Z)
• Towards Graph Foundation Models: A Survey and Beyond [66.37994863159861]
  Foundation models have emerged as critical components in a variety of artificial intelligence applications. The capabilities of foundation models to generalize and adapt motivate graph machine learning researchers to discuss the potential of developing a new graph learning paradigm. This article introduces the concept of Graph Foundation Models (GFMs), and offers an exhaustive explanation of their key characteristics and underlying technologies.
  arXiv  Detail & Related papers  (2023-10-18T09:31:21Z)
• Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks [5.324479750432588]
  Topological Deep Learning (TDL) provides a framework to process and extract knowledge from data associated with complex systems. TDL has demonstrated theoretical and practical advantages that hold the promise of breaking ground in the applied sciences and beyond. The rapid growth of the TDL literature for relational systems has led to a lack of unification in notation and language across message-passing Topological Neural Network (TNN) architectures. This presents a real obstacle for building upon existing works and for deploying message-passing TNNs to new real-world problems.
  arXiv  Detail & Related papers  (2023-04-20T01:02:13Z)
• Topological Deep Learning: A Review of an Emerging Paradigm [13.922282370294392]
  Topological data analysis provides principled global descriptions of multi-dimensional data. We review the nascent field of topological deep learning by first revisiting the core concepts of TDA. We then explore how the use of TDA techniques has evolved over time to support deep learning frameworks.
  arXiv  Detail & Related papers  (2023-02-08T02:11:24Z)
• Learning Description Logic Ontologies. Five Approaches. Where Do They Stand? [14.650545418986058]
  We highlight machine learning and data mining approaches that have been proposed for the creation of description logic (DL) These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks.
  arXiv  Detail & Related papers  (2021-04-02T18:36:45Z)
• Deep Learning for Road Traffic Forecasting: Does it Make a Difference? [6.220008946076208]
  This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting.
  arXiv  Detail & Related papers  (2020-12-02T15:56:11Z)
• A Chain Graph Interpretation of Real-World Neural Networks [58.78692706974121]
  We propose an alternative interpretation that identifies NNs as chain graphs (CGs) and feed-forward as an approximate inference procedure. The CG interpretation specifies the nature of each NN component within the rich theoretical framework of probabilistic graphical models. We demonstrate with concrete examples that the CG interpretation can provide novel theoretical support and insights for various NN techniques.
  arXiv  Detail & Related papers  (2020-06-30T14:46:08Z)
• Towards Interpretable Deep Learning Models for Knowledge Tracing [62.75876617721375]
  We propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model. Experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions.
  arXiv  Detail & Related papers  (2020-05-13T04:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.