From Classical to Topological Neural Networks Under Uncertainty
Not on the map. Why?
Abstract (arXiv)
This chapter explores neural networks, topological data analysis, and topological deep learning techniques, alongside statistical Bayesian methods, for processing images, time series, and graphs to maximize the potential of artificial intelligence in the military domain. Throughout the chapter, we highlight practical applications spanning image, video, audio, and time-series recognition, fraud detection, and link prediction for graphical data, illustrating how topology-aware and uncertainty-aware models can enhance robustness, interpretability, and generalization.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 78 of its 146 references that are in the map. First posted on arXiv 10 Feb 2026.
Builds on
78 of 146 references.
References · 78
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
- 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
38 more
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
- 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
- 2016Variational Graph Auto-Encoderscited by 1,373
- 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,139
- 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
- 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
- 2019Neural Graph Collaborative Filteringcited by 660
- 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
- 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
- 2015Weight Uncertainty in Neural Networkscited by 554
- 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
- 2020Graph Neural Networks in Recommender Systems: A Surveycited by 493
- 2018Hypergraph Neural Networkscited by 482
- 2020Inductive Representation Learning on Temporal Graphscited by 454
- 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
- 2020Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Userscited by 336
- 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
- 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 302
- 2019Representation Learning for Dynamic Graphs: A Surveycited by 260
- 2015Persistence Images: A Stable Vector Representation of Persistent Homologycited by 235
- 2015Beyond Short Snippets: Deep Networks for Video Classificationcited by 233
- 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
- 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
- 2022Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNscited by 208
- 2017CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filterscited by 203
- 2017An introduction to Topological Data Analysis: fundamental and practical aspects for data scientistscited by 171
- 2018Graph Convolution over Pruned Dependency Trees Improves Relation Extractioncited by 146
- 2017Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelingcited by 137
- 2019Stability Properties of Graph Neural Networkscited by 125
- 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 124
- 2020Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A surveycited by 123
- 2019Fake News Detection on Social Media using Geometric Deep Learningcited by 115
- 2017Deep Learning with Topological Signaturescited by 113
- 2020Simplicial Neural Networkscited by 105
- 2018Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Diseasecited by 98
50 most cited of 78 All 78 (JSON)
Built on by
Not cited yet.