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We introduce a class of neural networks named PLEIADES (PoLynomial Expansion In Adaptive Distributed Event-based Systems), which contains temporal convolution kernels generated from orthogonal polynomial basis functions.
Spiking Neuron Models: Single Neurons, Populations, Plasticity
Wulfram Gerstner and Werner M. Kistler · 2002
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Neuronal dynamics: From single neurons to networks and models of cognition
Wulfram Gerstner, Werner M Kistler, Richard Naud, and Liam Paninski · 2014
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
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Temporal convolutional networks: A unified approach to action segmentation
Colin Lea, Rene Vidal, Austin Reiter, and Gregory D Hager · 2016
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A low power, fully event-based gesture recognition system
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Temporal convolutional networks for action segmentation and detection
Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Learning spatio-temporal representation with pseudo-3d residual networks
Zhaofan Qiu, Ting Yao, and Tao Mei · 2017
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Event-based vision meets deep learning on steering prediction for self-driving cars
Ana I Maqueda, Antonio Loquercio, Guillermo Gallego, Narciso García, and Davide Scaramuzza · 2018
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SLAYER: Spike layer error reassignment in time
Sumit B Shrestha and Garrick Orchard · 2018
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A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri · 2018
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End-to-end learning of representations for asynchronous event-based data
Daniel Gehrig, Antonio Loquercio, Konstantinos G Derpanis, and Davide Scaramuzza · 2019
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Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras
Ariel Gordon, Hanhan Li, Rico Jonschkowski, and Anelia Angelova · 2019
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Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
Emre O Neftci, Hesham Mostafa, and Friedemann Zenke · 2019
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Tcnn: Temporal convolutional neural network for real-time speech enhancement in the time domain
Ashutosh Pandey and DeLiang Wang · 2019
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Legendre Memory Units: Continuous-time representation in recurrent neural networks
Aaron Voelker, Ivana Kajić, and Chris Eliasmith · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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Unsupervised event-based learning of optical flow, depth, and egomotion
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney, and Kostas Daniilidis · 2019
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S4ND: Modeling images and videos as multidimensional signals using state spaces
Eric Nguyen, Karan Goel, Albert Gu, Gordon W Downs, Preey Shah, Tri Dao, Stephen A Baccus, and Christopher Ré · 2022
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Simple hardware-efficient long convolutions for sequence modeling
Daniel Y Fu, Elliot L Epstein, Eric Nguyen, Armin W Thomas, Michael Zhang, Tri Dao, Atri Rudra, and Christopher Ré · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Get: Group event transformer for event-based vision
Yansong Peng, Yueyi Zhang, Zhiwei Xiong, Xiaoyan Sun, and Feng Wu · 2023
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Hyena hierarchy: Towards larger convolutional language models
Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, and Christopher Ré · 2023
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Event-based vision: A survey
Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, Jörg Conradt, Kostas Daniilidis, et al · 2020
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Hippo: Recurrent memory with optimal polynomial projections
Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré · 2020
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Efficient processing of spatio-temporal data streams with spiking neural networks
Alexander Kugele, Thomas Pfeil, Michael Pfeiffer, and Elisabetta Chicca · 2020
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Learning to detect objects with a 1 megapixel event camera
Etienne Perot, Pierre De Tournemire, Davide Nitti, Jonathan Masci, and Amos Sironi · 2020
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Hyper-optimized tensor network contraction
Johnnie Gray and Stefanos Kourtis · 2021
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CKConv: Continuous kernel convolution for sequential data
David W Romero, Anna Kuzina, Erik J Bekkers, Jakub M Tomczak, and Mark Hoogendoorn · 2021
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Discrete function bases and convolutional neural networks
Andreas Stöckel · 2021
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A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera
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State space models for event cameras
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