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08/16/2023
Inherent Redundancy in Spiking Neural Networks
Spiking Neural Networks (SNNs) are well known as a promising energy-effi...
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08/12/2023
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Spiking neural networks (SNNs) are emerging as an energy-efficient alter...
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07/21/2023
Deep Directly-Trained Spiking Neural Networks for Object Detection
Spiking neural networks (SNNs) are brain-inspired energy-efficient model...
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07/04/2023
Spike-driven Transformer
Spiking Neural Networks (SNNs) provide an energy-efficient deep learning...
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06/08/2023
Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis
Structural magnetic resonance imaging (sMRI) has shown great clinical va...
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05/20/2023
Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network
The Lottery Ticket Hypothesis (LTH) states that a randomly-initialized l...
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01/27/2023
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway
Due to the binary spike signals making converting the traditional high-p...
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11/22/2022
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network
Event cameras are considered to have great potential for computer vision...
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11/17/2022
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
The main streams of human activity recognition (HAR) algorithms are deve...
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09/28/2022
Attention Spiking Neural Networks
Benefiting from the event-driven and sparse spiking characteristics of t...
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07/08/2022
Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations
Zero-shot learning (ZSL) aims to recognize classes that do not have samp...
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03/28/2022
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
Recently, vision-language pre-training shows great potential in open-voc...
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03/02/2022
Rethinking Pretraining as a Bridge from ANNs to SNNs
Spiking neural networks (SNNs) are known as a typical kind of brain-insp...
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02/10/2022
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Graph neural networks (GNNs) have been a hot spot of recent research and...
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12/15/2021
Advancing Residual Learning towards Powerful Deep Spiking Neural Networks
Despite the rapid progress of neuromorphic computing, inadequate capacit...
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12/09/2021
Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks
Despite the rapid progress of neuromorphic computing, the inadequate dep...
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10/23/2021
ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks
With event-driven algorithms, especially the spiking neural networks (SN...
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08/30/2021
Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions
Visual recognition is currently one of the most important and active res...
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07/25/2021
H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks
Although spiking neural networks (SNNs) take benefits from the bio-plaus...
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07/25/2021
Temporal-wise Attention Spiking Neural Networks for Event Streams Classification
How to effectively and efficiently deal with spatio-temporal event strea...
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06/30/2021
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
Biological spiking neurons with intrinsic dynamics underlie the powerful...
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05/27/2021
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization
Huge computational costs brought by convolution and batch normalization ...
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03/10/2021
Sampling methods for efficient training of graph convolutional networks: A survey
Graph Convolutional Networks (GCNs) have received significant attention ...
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11/30/2020
Training and Inference for Integer-Based Semantic Segmentation Network
Semantic segmentation has been a major topic in research and industry in...
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11/12/2020
LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing
Spiking neural networks (SNNs) based on Leaky Integrate and Fire (LIF) m...
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10/29/2020
Going Deeper With Directly-Trained Larger Spiking Neural Networks
Spiking neural networks (SNNs) are promising in a bio-plausible coding f...
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10/22/2020
Restoring Negative Information in Few-Shot Object Detection
Few-shot learning has recently emerged as a new challenge in the deep le...
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08/21/2020
Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs
Recurrent neural networks (RNNs) are powerful in the tasks oriented to s...
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06/29/2020
Hybrid Tensor Decomposition in Neural Network Compression
Deep neural networks (DNNs) have enabled impressive breakthroughs in var...
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06/05/2020
Brain-inspired global-local hybrid learning towards human-like intelligence
The combination of neuroscience-oriented and computer-science-oriented a...
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05/02/2020
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Neuromorphic data, recording frameless spike events, have attracted cons...
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01/01/2020
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Recently, backpropagation through time inspired learning algorithms are ...
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01/01/2020
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks
In recent years, plenty of metrics have been proposed to identify networ...
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12/28/2019
Transfer Learning in General Lensless Imaging through Scattering Media
Recently deep neural networks (DNNs) have been successfully introduced t...
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12/20/2019
Bridging adversarial samples and adversarial networks
Generative adversarial networks have achieved remarkable performance on ...
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12/08/2019
Lossless Compression for 3DCNNs Based on Tensor Train Decomposition
Three dimensional convolutional neural networks (3DCNNs) have been appli...
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11/03/2019
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Spiking neural network is an important family of models to emulate the b...
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09/05/2019
Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers
Deep neural network (DNN) quantization converting floating-point (FP) da...
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07/02/2019
A Hybrid Learning Rule for Efficient and Rapid Inference with Spiking Neural Networks
The emerging neuromorphic computing (NC) architectures have shown compel...
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07/02/2019
A Tandem Learning Rule for Efficient and Rapid Inference on Deep Spiking Neural Networks
Emerging neuromorphic computing (NC) architectures have shown compelling...
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02/15/2019
Deep Spiking Neural Network with Spike Count based Learning Rule
Deep spiking neural networks (SNNs) support asynchronous event-driven co...
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10/25/2018
Batch Normalization Sampling
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normal...
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10/01/2018
Dynamic Sparse Graph for Efficient Deep Learning
We propose to execute deep neural networks (DNNs) with dynamic and spars...
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09/16/2018
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Spiking neural networks (SNNs) are gaining more attention as a promising...
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07/25/2018
Crossbar-aware neural network pruning
Crossbar architecture based devices have been widely adopted in neural n...
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02/27/2018
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Batch Normalization (BN) has been proven to be quite effective at accele...
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02/13/2018
Training and Inference with Integers in Deep Neural Networks
Researches on deep neural networks with discrete parameters and their de...
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02/07/2018
Super-resolution of spatiotemporal event-based image
Super-resolution (SR) is a useful technology to generate a high-resoluti...
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06/08/2017
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks...
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05/25/2017
