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Found 13 papers, 5 papers with code
General Automatic Solution Generation of Social Problems
Given the escalating intricacy and multifaceted nature of contemporary social systems, manually generating solutions to address pertinent social issues has become a formidable task.
Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention
In this paper, we propose a brain-inspired hybrid neural network (HNN) that introduces temporal binding theory originated from neuroscience into ANNs by integrating spike timing dynamics (via spiking neural networks, SNNs) with reconstructive attention (by ANNs).
Brain-inspired global-local learning incorporated with neuromorphic computing
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
Adversarial symmetric GANs: bridging adversarial samples and adversarial networks
Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability.
BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS
Generative adversarial networks have achieved remarkable performance on various tasks but suffer from sensitivity to hyper-parameters, training instability, and mode collapse.
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking
To the best of our knowledge, DashNet is the first framework that can integrate and process ANNs and SNNs in a hybrid paradigm, which provides a novel solution to achieve both effectiveness and efficiency for high-speed object tracking.
Convolution with even-sized kernels and symmetric padding
Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training process.
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention.
Robust event-stream pattern tracking based on correlative filter
Object tracking based on retina-inspired and event-based dynamic vision sensor (DVS) is challenging for the noise events, rapid change of event-stream shape, chaos of complex background textures, and occlusion.
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs).
Training and Inference with Integers in Deep Neural Networks
Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics.
Super-resolution of spatiotemporal event-stream image captured by the asynchronous temporal contrast vision sensor
Firstly, the event number of each pixel of the HR DVS image is determined with a sparse signal representation based method to obtain the HR event-count map from that of the LR DVS recording.
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology.
