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Found 6 papers, 2 papers with code
Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout
Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown increasing promise for energy-efficient applications in neuromorphic hardware.
Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time
When combined with a novel dynamic spiking neuron model, the Liquid-Time-Constant neuron, we show that SNNs trained with FPTT outperform online BPTT approximations, and approach or exceed offline BPTT accuracy on temporal classification tasks.
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons' energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs).
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Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware.
LocalNorm: Robust Image Classification through Dynamically Regularized Normalization
While modern convolutional neural networks achieve outstanding accuracy on many image classification tasks, they are, compared to humans, much more sensitive to image degradation.
An image representation based convolutional network for DNA classification
The folding structure of the DNA molecule combined with helper molecules, also referred to as the chromatin, is highly relevant for the functional properties of DNA.
