Daniel Neil
Daniel Neil
Verified email at benevolent.ai
Cited by
Cited by
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
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
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
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
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
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
DDD17: End-to-end DAVIS driving dataset
J Binas, D Neil, SC Liu, T Delbruck
arXiv preprint arXiv:1711.01458, 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
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
Effective Sensor Fusion with Event-Based Sensors and Deep Network Architectures
D Neil, SC Liu
IEEE Int. Symposium on Circuits and Systems (ISCAS), 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
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
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
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
Exploring deep recurrent models with reinforcement learning for molecule design
D Neil, M Segler, L Guasch, M Ahmed, D Plumbley, M Sellwood, N Brown
Delta networks for optimized recurrent network computation
D Neil, JH Lee, T Delbruck, SC Liu
International Conference on Machine Learning, 2584-2593, 2017
Feature representations for neuromorphic audio spike streams
J Anumula, D Neil, T Delbruck, SC Liu
Frontiers in neuroscience 12, 23, 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
Multi-channel attention for end-to-end speech recognition
S Braun, D Neil, J Anumula, E Ceolini, SC Liu
2018 Interspeech, 0-0, 2018
Event-driven sensing for efficient perception: Vision and audition algorithms
SC Liu, B Rueckauer, E Ceolini, A Huber, T Delbruck
IEEE Signal Processing Magazine 36 (6), 29-37, 2019
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