| Fast-Classifying, High-Accuracy Spiking Deep Networks Through Weight and Threshold Balancing PU Diehl, D Neil, J Binas, M Cook, SC Liu, M Pfeiffer International Joint Conference on Neural Networks (IJCNN), 2015 | 461 | 2015 |
| Real-time classification and sensor fusion with a spiking deep belief network P O'Connor, D Neil, SC Liu, T Delbruck, M Pfeiffer Frontiers in neuroscience 7, 178, 2013 | 365 | 2013 |
| Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences D Neil, M Pfeiffer, SC Liu Advances in Neural Information Processing Systems (NIPS), 2016 29, 2016 | 243 | 2016 |
| Minitaur, an event-driven FPGA-based spiking network accelerator D Neil, SC Liu IEEE Transactions on Very Large Scale Integration (VLSI) Systems 22 (12 …, 2014 | 132 | 2014 |
| Robustness of spiking deep belief networks to noise and reduced bit precision of neuro-inspired hardware platforms E Stromatias, D Neil, M Pfeiffer, F Galluppi, SB Furber, SC Liu Frontiers in neuroscience 9, 222, 2015 | 84 | 2015 |
| Steering a predator robot using a mixed frame/event-driven convolutional neural network DP Moeys, F Corradi, E Kerr, P Vance, G Das, D Neil, D Kerr, T Delbrück 2016 Second International Conference on Event-based Control, Communication …, 2016 | 78 | 2016 |
| DeltaRNN: A power-efficient recurrent neural network accelerator C Gao, D Neil, E Ceolini, SC Liu, T Delbruck Proceedings of the 2018 ACM/SIGDA International Symposium on Field …, 2018 | 69 | 2018 |
| DDD17: End-to-end DAVIS driving dataset J Binas, D Neil, SC Liu, T Delbruck arXiv preprint arXiv:1711.01458, 2017 | 65 | 2017 |
| Scalable Energy-Efficient, Low-Latency Implementations of Spiking Deep Belief Networks on SpiNNaker E Stromatias, D Neil, F Galluppi, M Pfeiffer, SC Liu, S Furber IEEE International Joint Conference on Neural Networks (IJCNN), 2015 | 63* | 2015 |
| Effective Sensor Fusion with Event-Based Sensors and Deep Network Architectures D Neil, SC Liu IEEE Int. Symposium on Circuits and Systems (ISCAS), 2016 | 55 | 2016 |
| Combined frame- and event-based detection and tracking H Liu, DP Moeys, D Neil, SC Liu, T Delbruck IEEE Int. Symposium on Circuits and Systems (ISCAS), 2016 | 42 | 2016 |
| Learning to be Efficient: Algorithms for Training Low-Latency, Low-Compute Deep Spiking Neural Networks D Neil, M Pfeiffer, SC Liu ACM Symposium on Applied Computing 31, 2016 | 38 | 2016 |
| Delta networks for optimized recurrent network computation D Neil, JH Lee, T Delbruck, SC Liu International Conference on Machine Learning, 2584-2593, 2017 | 35 | 2017 |
| LSI design method having dummy pattern generation process and LCR extraction process and computer program therefor H Ohba, J Watanabe US Patent 6,779,164, 2004 | 35* | 2004 |
| Exploring deep recurrent models with reinforcement learning for molecule design D Neil, M Segler, L Guasch, M Ahmed, D Plumbley, M Sellwood, N Brown | 34 | 2018 |
| A curriculum learning method for improved noise robustness in automatic speech recognition S Braun, D Neil, SC Liu 2017 25th European Signal Processing Conference (EUSIPCO), 548-552, 2017 | 32 | 2017 |
| Feature representations for neuromorphic audio spike streams J Anumula, D Neil, T Delbruck, SC Liu Frontiers in neuroscience 12, 23, 2018 | 28 | 2018 |
| Precise deep neural network computation on imprecise low-power analog hardware J Binas, D Neil, G Indiveri, SC Liu, M Pfeiffer arXiv: Computer Science/Neural and Evolutionary Computing 1606 (1606.07786), 0-0, 2016 | 26 | 2016 |
| Multi-channel attention for end-to-end speech recognition S Braun, D Neil, J Anumula, E Ceolini, SC Liu 2018 Interspeech, 0-0, 2018 | 19 | 2018 |
| Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs D Neil, J Briody, A Lacoste, A Sim, P Creed, A Saffari Proceedings of the Machine Learning for Health (ML4H) Workshop at NeurIPS 2018, 2018 | 16 | 2018 |
Shih-Chii LiuGroup Leader, University of Zurich / ETH ZurichVerified email at ini.phys.ethz.ch
