mlsanityData to 14 Oct 2026

Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists

arXiv 2305.05601 ↗First posted 9 May 2023cs.LG math-ph math.MPcited by 3

Not on the map. Why?

Abstract (arXiv)

In this expository paper we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successfull algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks. We go over the key ingredients for these algorithms: the score and loss function and we explain the main steps for the training of a model. We do not aim to give a complete and exhaustive treatment, but we isolate few concepts to give a fast introduction to the subject. We provide some appendices to complement our treatment discussing Kullback-Leibler divergence, regression, Multi-layer Perceptrons and the Universal Approximation Theorem.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 42 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

19 of 42 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  4. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  5. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  6. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  7. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,070
  8. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  9. 2014New insights and perspectives on the natural gradient methodcited by 443
  10. 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
  11. 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 214
  12. 2017Stochastic gradient descent performs variational inference, converges to limit cycles for deep networkscited by 109
7 more
  1. 2020Sheaf Neural Networkscited by 84
  2. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  3. 2015AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discoverycited by 38
  4. 2022Statistical Properties of the log-cosh Loss Function Used in Machine Learningcited by 16
  5. 2021Model-centric Data Manifold: the Data Through the Eyes of the Modelcited by 9
  6. 2019Horizontal Flows and Manifold Stochastics in Geometric Deep Learningcited by 6
  7. 2017Distributed learning of CNNs on heterogeneous CPU/GPU architecturescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.

3 papers.

  1. 2023Graph Neural Networks and Time Series as Directed Graphs for Quality Recognitioncited by 0
  2. 2025Algebraic Adversarial Attacks on Explainability Modelscited by 0
  3. 2026Sheaf Neural Networks and biomedical applicationscited by 0