| Pyro: Deep universal probabilistic programming E Bingham, JP Chen, M Jankowiak, F Obermeyer, N Pradhan, ... Journal of machine learning research 20 (28), 1-6, 2019 | 1248 | 2019 |
| Conditional similarity networks A Veit, S Belongie, T Karaletsos Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 188 | 2017 |
| RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints Y Zhong, T Karaletsos, P Drewe, VT Sreedharan, D Kuo, K Singh, ... Bioinformatics 33 (1), 139-141, 2017 | 159 | 2017 |
| Likelihood-free inference with emulator networks JM Lueckmann, G Bassetto, T Karaletsos, JH Macke Symposium on Advances in Approximate Bayesian Inference, 32-53, 2019 | 125 | 2019 |
| Bayesian representation learning with oracle constraints T Karaletsos, S Belongie, G Rätsch International Conference On Learning Representations (ICLR) 2016, 2015 | 113 | 2015 |
| Generalized Hidden Parameter MDPs: Transferable Model-based RL in a Handful of Trials CF Perez, FP Such, T Karaletsos AAAI Conference on Artificial Intelligence 2020, 2020 | 45 | 2020 |
| An empirical analysis of topic modeling for mining cancer clinical notes KR Chan, X Lou, T Karaletsos, C Crosbie, S Gardos, D Artz, G Rätsch 2013 IEEE 13th International Conference on Data Mining Workshops, 56-63, 2013 | 40 | 2013 |
| Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights T Karaletsos, TD Bui Neural Information Processing Systems (NeurIPS) 2020, 2020 | 28 | 2020 |
| Probabilistic meta-representations of neural networks T Karaletsos, P Dayan, Z Ghahramani arXiv preprint arXiv:1810.00555, 2018 | 27 | 2018 |
| Adversarial message passing for graphical models T Karaletsos Advances in Approximate Inference Workshop (AABI) at Neurips 2016, 2016 | 24 | 2016 |
| Variational Auto-Regressive Gaussian Processes for Continual Learning S Kapoor, T Karaletsos, TD Bui International Conference on Machine Learning 2021 ; arXiv preprint arXiv …, 2020 | 23 | 2020 |
| Efficient transfer learning and online adaptation with latent variable models for continuous control CF Perez, FP Such, T Karaletsos Workshop on Continual Learning Neurips 2018, 2018 | 18 | 2018 |
| Intelligent regularization of neural network architectures Z Ghahramani, D Bemis, T Karaletsos US Patent 11,164,076, 2021 | 17 | 2021 |
| Pathwise Derivatives for Multivariate Distributions M Jankowiak, T Karaletsos AISTATS 2019, 2018 | 14 | 2018 |
| Event detection using sensor data NP Volk, T Karaletsos, U Madhow, JB Yosinski, TR Sumers US Patent App. 16/233,779, 2019 | 11 | 2019 |
| Automatic relevance determination for deep generative models T Karaletsos, G Rätsch arXiv preprint arXiv:1505.07765, 2015 | 10 | 2015 |
| TyXe: Pyro-based Bayesian neural nets for Pytorch H Ritter, T Karaletsos Proceedings of Machine Learning and Systems 4, 398-413, 2022 | 9 | 2022 |
| A generative model of words and relationships from multiple sources SL Hyland, T Karaletsos, G Rätsch AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2015 | 9 | 2015 |
| ShapePheno: unsupervised extraction of shape phenotypes from biological image collections T Karaletsos, O Stegle, C Dreyer, J Winn, KM Borgwardt Bioinformatics 28 (7), 1001-1008, 2012 | 9 | 2012 |
| Representations of units in neural networks T Karaletsos, P Dayan, Z Ghahramani US Patent App. 16/356,991, 2019 | 8 | 2019 |
Gunnar RätschProfessor, ETH Zürich在 inf.ethz.ch 的电子邮件经过验证
