| Scalable inference in sdes by direct matching of the fokker–planck–kolmogorov equation A Solin, E Tamir, P Verma Advances in Neural Information Processing Systems 34, 417-429, 2021 | 16 | 2021 |
| Transport with support: Data-conditional diffusion bridges E Tamir, M Trapp, A Solin arXiv preprint arXiv:2301.13636, 2023 | 8 | 2023 |
| Sparse function-space representation of neural networks A Scannell, R Mereu, P Chang, E Tamir, J Pajarinen, A Solin arXiv preprint arXiv:2309.02195, 2023 | 3 | 2023 |
| Function-space Parameterization of Neural Networks for Sequential Learning A Scannell, R Mereu, P Chang, E Tamir, J Pajarinen, A Solin arXiv preprint arXiv:2403.10929, 2024 | 1 | 2024 |
| Learning to Approximate Particle Smoothing Trajectories via Diffusion Generative Models E Tamir, A Solin arXiv preprint arXiv:2406.00561, 2024 | | 2024 |
| The Beurling Transform and Calculus of Variations through the Burkholder Functional E Tamir Master's thesis, University of Helsinki, 2017 | | 2017 |
| Conditional Flow Matching for Time Series Modelling E Tamir, N Laabid, M Heinonen, V Garg, A Solin ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative …, 0 | | |
| Data-Conditional Diffusion Bridges E Tamir, M Trapp, A Solin NeurIPS 2023 Workshop Optimal Transport and Machine Learning, 0 | | |
| NeuralNetworks as Sparse Gaussian Processes for SequentialLearning A Scannell, R Mereu, P Chang, E Tamir, J Pajarinen, A Solin | | |
