| Multiagent cooperation and competition with deep reinforcement learning A Tampuu, T Matiisen, D Kodelja, I Kuzovkin, K Korjus, J Aru, J Aru, ... PloS one 12 (4), e0172395, 2017 | 1279 | 2017 |
| A survey of end-to-end driving: Architectures and training methods A Tampuu, T Matiisen, M Semikin, D Fishman, N Muhammad IEEE Transactions on Neural Networks and Learning Systems 33 (4), 1364-1384, 2020 | 399 | 2020 |
| ViraMiner: deep learning on raw DNA sequences for identifying viral genomes in human samples A Tampuu, Z Bzhalava, J Dillner, R Vicente Plos One, 2019 | 157 | 2019 |
| Machine Learning for detection of viral sequences in human metagenomic datasets Z Bzhalava, A Tampuu, P Bała, R Vicente, J Dillner BMC bioinformatics 19 (1), 336, 2018 | 57 | 2018 |
| Efficient neural decoding of self-location with a deep recurrent network A Tampuu, T Matiisen, HF Ólafsdóttir, C Barry, R Vicente PLoS computational biology 15 (2), e1006822, 2019 | 45 | 2019 |
| Lidar-as-camera for end-to-end driving A Tampuu, R Aidla, JA van Gent, T Matiisen Sensors 23 (5), 2845, 2023 | 27 | 2023 |
| Perspective taking in deep reinforcement learning agents A Labash, J Aru, T Matiisen, A Tampuu, R Vicente Frontiers in Computational Neuroscience 14, 69, 2020 | 18 | 2020 |
| Common clinical blood and urine biomarkers for ischemic stroke: an Estonian Electronic Health Records database study S Kurvits, A Harro, A Reigo, A Ott, S Laur, D Särg, A Tampuu, ... European Journal of Medical Research 28 (1), 133, 2023 | 9 | 2023 |
| The effects of speed and delays on test-time performance of end-to-end self-driving A Tampuu, K Roosild, I Uduste Sensors 24 (6), 1963, 2024 | 6 | 2024 |
| Controlling Steering with Energy-Based Models M Baliesnyi, A Tampuu, T Matiisen NeurIPS 2022 Workshop on Machine Learning for Autonomous Driving (ML4AD), 2022 | 3 | 2022 |
| Apes: a python toolbox for simulating reinforcement learning environments A Labash, A Tampuu, T Matiisen, J Aru, R Vicente arXiv preprint arXiv:1808.10692, 2018 | 3 | 2018 |
| Replicating the Paper “Playing Atari with Deep Reinforcement Learning.” K Korjus, I Kuzovkin, A Tampuu, T Pungas Technical Report, Introduction to Computational Neuroscience, University of …, 2013 | 2 | 2013 |
| Neural networks for analyzing biological data A Tampuu University of Tartu, 2020 | | 2020 |
| Two Samples Are Enough: Verbal Confidence Meets Self-Consistency in Reasoning LLMs M Del, M Kängsepp, M Domnich, A Tampuu, L Yankovskaya, M Kull, ... | | |
| Learning DNA mutational signatures using neural networks RV Zafra, L Parts, T Matiisen, A Tampuu | | |
| Replicating the Paper “Playing Atari with Deep Reinforcement Learning”[MKS K Korjus, I Kuzovkin, A Tampuu, T Pungas | | |
