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 | 160 | 2020 |
Nonlinear dynamic Boltzmann machines for time-series prediction S Dasgupta, T Osogami Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 88 | 2017 |
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 | 86 | 2019 |
Learning machine based detection of abnormal network performance JP Vasseur, G Mermoud, S Dasgupta US Patent 9,628,362, 2017 | 75 | 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 | 71 | 2020 |
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 | 63 | 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 | 56 | 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 | 45 | 2017 |
Learning mixtures of Gaussians using the k-means algorithm K Chaudhuri, S Dasgupta, A Vattani arXiv preprint arXiv:0912.0086, 2009 | 42 | 2009 |
A neurocomputational model of goal-directed navigation in insect-inspired artificial agents D Goldschmidt, P Manoonpong, S Dasgupta Frontiers in Neurorobotics 11, 20, 2017 | 41 | 2017 |
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 |
Diameter-based active learning C Tosh, S Dasgupta International Conference on Machine Learning, 3444-3452, 2017 | 34 | 2017 |
The use of hebbian cell assemblies for nonlinear computation C Tetzlaff, S Dasgupta, T Kulvicius, F Wörgötter Scientific reports 5 (1), 12866, 2015 | 34 | 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 | 34 | 2014 |
Internal model from observations for reward shaping D Kimura, S Chaudhury, R Tachibana, S Dasgupta arXiv preprint arXiv:1806.01267, 2018 | 23 | 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 | 21 | 2016 |
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 | 19 | 2017 |
Learning from discriminative feature feedback S Dasgupta, A Dey, N Roberts, S Sabato Advances in Neural Information Processing Systems 31, 2018 | 18 | 2018 |
Cooperative neural network reinforcement learning S Dasgupta, T Morimura, T Osogami US Patent App. 15/647,543, 2019 | 15 | 2019 |