| Continual Classification Learning Using Generative Models F Lavda, J Ramapuram, M Gregorova, A Kalousis 32nd Conference on Neural Information Processing Systems (NIPS 2018 …, 2018 | 62 | 2018 |
| Variational Saccading: Efficient Inference for Large Resolution Images J Ramapuram, M Diephuis, L Frantzeska, R Webb, A Kalousis BMVC 2019 & Bayesian Deep Learning Workshop Neurips, 2018, 2018 | 9 | 2018 |
| Data-dependent conditional priors for unsupervised learning of multimodal data F Lavda, M Gregorová, A Kalousis Entropy 22 (8), 888, 2020 | 5 | 2020 |
| Improving VAE generations of multimodal data through data-dependent conditional priors F Lavda, M Gregorová, A Kalousis European Conference of Artificial Intelligence, ECAI 2020, 1254-1261, 2019 | 5 | 2019 |
| Improving VAE Generations of Multimodal Data Through Data-Dependent Conditional Priors F Lavda, M Gregorová, A Kalousis ECAI 2020, 1254-1261, 2020 | 2 | 2020 |
| Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation F Lavda, A Kalousis Entropy 25 (12), 1659, 2023 | 1 | 2023 |
| Back translation variational autoencoders for OOD generation F Lavda, A Kalousis ICLR 2023 Workshop on Domain Generalization, 2023 | 1 | 2023 |
| GLAD: Improving Latent Graph Generative Modeling with Simple Quantization VK Nguyen, Y Boget, F Lavda, A Kalousis arXiv preprint arXiv:2403.16883, 2024 | | 2024 |
| Discrete Latent Graph Generative Modeling with Diffusion Bridges VK Nguyen, Y Boget, F Lavda, A Kalousis arXiv preprint arXiv:2403.16883, 2024 | | 2024 |
| Discrete Latent Graph Generative Modeling with Diffusion Bridges Y Boget, F Lavda, A Kalousis CoRR, 2024 | | 2024 |
| Improving the capabilities of Variational Autoencoder Models by exploring their latent space F Lavda Université de Genève, 2024 | | 2024 |
| Variational saccading J Ramapuram, M Diephuis, F Lavda, R Webb, A Kalousis Proceedings of the 30th British Machine Vision Conference, 2019 | | 2019 |
| GLAD: Improving Latent Graph Generative Modeling with Simple Quantization Y Boget, F Lavda, A Kalousis ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative …, 0 | | |
| A Study of Recurrent neural networks (RNNs) in univariate and multivariate time series F Lavda | | |
Alexandros KalousisUniversity of Applied Sciences, Western Switzerland (HES-SO).Verified email at hesge.ch
