| Hardware/software co-exploration of neural architectures W Jiang, L Yang, EHM Sha, Q Zhuge, S Gu, S Dasgupta, Y Shi, J Hu IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2020 | 175 | 2020 |
| Model-based deep reinforcement learning for dynamic portfolio optimization P Yu, JS Lee, I Kulyatin, Z Shi, S Dasgupta arXiv preprint arXiv:1901.08740, 2019 | 92 | 2019 |
| Nonlinear dynamic Boltzmann machines for time-series prediction S Dasgupta, T Osogami Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 92 | 2017 |
| Standing on the shoulders of giants: Hardware and neural architecture co-search with hot start W Jiang, L Yang, S Dasgupta, J Hu, Y Shi IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2020 | 80 | 2020 |
| Learning machine based detection of abnormal network performance JP Vasseur, G Mermoud, S Dasgupta US Patent 9,628,362, 2017 | 76 | 2017 |
| Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation G Ren, W Chen, S Dasgupta, C Kolodziejski, F Wörgötter, P Manoonpong Information Sciences 294, 666–682, 2015 | 66 | 2015 |
| Transfer learning from synthetic to real images using variational autoencoders for precise position detection T Inoue, S Choudhury, G De Magistris, S Dasgupta 2018 25th IEEE International Conference on Image Processing (ICIP), 2725-2729, 2018 | 58 | 2018 |
| Information dynamics based self-adaptive reservoir for delay temporal memory tasks S Dasgupta, F Wörgötter, P Manoonpong Evolving Systems 4, 235-249, 2013 | 48 | 2013 |
| Scheduling predictive models for machine learning systems JP Vasseur, S Dasgupta, G Mermoud US Patent 9,794,145, 2017 | 46 | 2017 |
| A neurocomputational model of goal-directed navigation in insect-inspired artificial agents D Goldschmidt, P Manoonpong, S Dasgupta Frontiers in neurorobotics 11, 20, 2017 | 44 | 2017 |
| Learning mixtures of Gaussians using the k-means algorithm K Chaudhuri, S Dasgupta, A Vattani arXiv preprint arXiv:0912.0086, 2009 | 42 | 2009 |
| The use of hebbian cell assemblies for nonlinear computation C Tetzlaff, S Dasgupta, T Kulvicius, F Wörgötter Scientific reports 5 (1), 12866, 2015 | 37 | 2015 |
| Distributed Recurrent Neural Forward Models with Synaptic Adaptation and CPG-based control for Complex Behaviors of Walking Robots S Dasgupta, D Goldschmidt, F Wörgötter, P Manoonpong Frontiers in Neurorobotics 9 (10), 17, 2015 | 36 | 2015 |
| Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control S Dasgupta, F Wörgötter, P Manoonpong Frontiers in neural circuits 8, 126, 2014 | 36 | 2014 |
| Diameter-based active learning C Tosh, S Dasgupta International Conference on Machine Learning, 3444-3452, 2017 | 35 | 2017 |
| Internal model from observations for reward shaping D Kimura, S Chaudhury, R Tachibana, S Dasgupta arXiv preprint arXiv:1806.01267, 2018 | 24 | 2018 |
| 25th annual computational neuroscience meeting: CNS-2016 TO Sharpee, A Destexhe, M Kawato, V Sekulić, FK Skinner, DK Wójcik, ... BMC neuroscience 17, 1-112, 2016 | 24 | 2016 |
| Neural network cooperation S Dasgupta US Patent 11,593,611, 2023 | 22 | 2023 |
| Learning from discriminative feature feedback S Dasgupta, A Dey, N Roberts, S Sabato Advances in Neural Information Processing Systems 31, 2018 | 20 | 2018 |
| Transfer learning from synthetic to real images using variational autoencoders for robotic applications T Inoue, S Chaudhury, G De Magistris, S Dasgupta arXiv preprint arXiv:1709.06762, 2017 | 20 | 2017 |
Poramate ManoonpongVidyasirimedhi Institute of Science and Technology (VISTEC) | University of Southern Denmark (SDU)Bestätigte E-Mail-Adresse bei mmmi.sdu.dk
