| Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks P Esser, L Chennuru Vankadara, D Ghoshdastidar Advances in Neural Information Processing Systems (NeurIPS) 34, 2021 | 30 | 2021 |
| Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel M Sabanayagam, P Esser, D Ghoshdastidar Transactions on Machine Learning Research, 2023 | 8* | 2023 |
| New Insights into Graph Convolutional Networks using Neural Tangent Kernels M Sabanayagam, P Esser, D Ghoshdastidar ECMLPKDD 2022, 18th International Workshop on Mining and Learning with Graphs, 2021 | 8* | 2021 |
| Near-optimal comparison based clustering M Perrot, P Esser, D Ghoshdastidar Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | 6 | 2020 |
| Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds PM Esser, F Nielsen Advances in Neural Information Processing Systems 34, 13th Annual Workshop …, 2021 | 2 | 2021 |
| Non-Parametric Representation Learning with Kernels P Esser, M Fleissner, D Ghoshdastidar AAAI Conference on Artificial Intelligence (AAAI-24), 2023 | 1 | 2023 |
| Representation Learning Dynamics of Self-Supervised Models P Esser, S Mukherjee, D Ghoshdastidar arXiv preprint arXiv:2309.02011, 2023 | 1 | 2023 |
| When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? GG Anil, P Esser, D Ghoshdastidar arXiv preprint arXiv:2403.08673, 2024 | | 2024 |
| Improved Representation Learning Through Tensorized Autoencoders PM Esser, S Mukherjee, M Sabanayagam, D Ghoshdastidar International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 | | 2022 |
| On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models PM Esser, F Nielsen arXiv preprint arXiv:2206.08598, 2022 | | 2022 |
Debarghya GhoshdastidarTechnical University of MunichVerified email at cit.tum.de
