Ardi Tampuu
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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
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, 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
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), 1-11, 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
Perspective taking in deep reinforcement learning agents
A Labash, J Aru, T Matiisen, A Tampuu, R Vicente
Frontiers in Computational Neuroscience 14, 69, 2020
Replicating the Paper “Playing Atari with Deep Reinforcement Learning.”
K Korjus, I Kuzovkin, A Tampuu, T Pungas
Faculty of Mathematics and Computer Science at the University of Tartu …, 2014
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
LiDAR-as-Camera for End-to-End Driving
A Tampuu, R Aidla, JA van Gent, T Matiisen
arXiv preprint arXiv:2206.15170, 2022
Neural networks for analyzing biological data
A Tampuu
University of Tartu, 2020
A Survey of End-to-End Driving: Architectures and Training Methods Download PDF
A Tampuu, M Semikin, N Muhammad, D Fishman, T Matiisen
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
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