| Approximating the solution to wave propagation using deep neural networks WE Sorteberg, S Garasto, AS Pouplin, CD Cantwell, AA Bharath arXiv preprint arXiv:1812.01609, 2018 | 24 | 2018 |
| Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data A Creswell, A Pouplin, AA Bharath IET Computer Vision 12 (8), 1105-1111, 2018 | 21 | 2018 |
| Pulling back information geometry G Arvanitidis, M González-Duque, A Pouplin, D Kalatzis, S Hauberg arXiv preprint arXiv:2106.05367, 2021 | 15 | 2021 |
| Density estimation on smooth manifolds with normalizing flows D Kalatzis, JZ Ye, A Pouplin, J Wohlert, S Hauberg arXiv preprint arXiv:2106.03500, 2021 | 8 | 2021 |
| Stochman NS Detlefsen, A Pouplin, CW Feldager, C Geng, D Kalatzis, H Hauschultz, ... GitHub. Note: https://github. com/MachineLearningLifeScience/stochman 3, 4, 2021 | 6 | 2021 |
| On the curvature of the loss landscape A Pouplin, H Roy, SP Singh, G Arvanitidis arXiv preprint arXiv:2307.04719, 2023 | 2 | 2023 |
| Identifying latent distances with Finslerian geometry A Pouplin, D Eklund, CH Ek, S Hauberg arXiv preprint arXiv:2212.10010, 2022 | 2 | 2022 |
| PyRelationAL: a python library for active learning research and development P Scherer, A Pouplin, A Del Vecchio, O Bolton, J Soman, JP Taylor-King, ... arXiv preprint arXiv:2205.11117, 2022 | 1 | 2022 |
| Towards Variational Flow Matching on General Geometries O Zaghen, F Eijkelboom, A Pouplin, EJ Bekkers arXiv preprint arXiv:2502.12981, 2025 | | 2025 |
| Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop S Vadgama, E Bekkers, A Pouplin, SO Kaba, R Walters, H Lawrence, ... Geometry-grounded Representation Learning and Generative Modeling Workshop …, 2024 | | 2024 |
| PyRelationAL: A Library for Active Learning Research and Development P Scherer, T Gaudelet, A Pouplin, J Soman, L Edwards, JP Taylor-King CoRR, 2022 | | 2022 |
