Brain-inspired Intelligence

Multi-modal Attention Network for Stock Movements Prediction
Stock prices move as piece-wise trending fluctuation rather than a purely random walk. Traditionally, the prediction of future stock …
He Shwai, Gu Shi
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Multi-modal Attention Network for Stock Movements Prediction
Converting artificial neural networks to spiking neural networks via parameter calibration
Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the nextgeneration neural …
Li Yuhang, Deng Shikuang, Dong Xin, Gu Shi
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Converting artificial neural networks to spiking neural networks via parameter calibration
Temporal efficient training of spiking neural network via gradient re-weighting
Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest because of their event-driven and …
Deng Shikuang, Li Yuhang, Zhang Shanghang, Gu Shi
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Temporal efficient training of spiking neural network via gradient re-weighting
Differentiable spike:Rethinking gradient-descent for training spiking neural networks
Spiking Neural Networks (SNNs) have emerged as a biology-inspired method mimicking the spiking nature of brain neurons. This …
Li Yuhang, Guo Yufei, Zhang Shanghang, Deng Shikuang, Hai Yongqing, Gu Shi
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Differentiable spike:Rethinking gradient-descent for training spiking neural networks
Effective Ensemble of Deep Neural Networks Predicts Neural Responses to Naturalistic Videos
This report provides a review of our submissions to the Algonauts Challenge 2021. In this challenge, neural responses in the visual …
Yang Huzheng, Zhang Shanghang, Wu Yifan, Li Yuanning, Gu Shi
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Effective Ensemble of Deep Neural Networks Predicts Neural Responses to Naturalistic Videos
A free lunch from ANN:Towards efficient, accurate spiking neural networks calibration
Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted …
Li Yuhang, Deng Shikuang, Dong Xin, Gong Ruihao, Gu Shi
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A free lunch from ANN:Towards efficient, accurate spiking neural networks calibration
Brecq:Pushing the limit of post-training quantization by block reconstruction
We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). …
Li Yuhang, Gong Ruihao, Tan Xu, Yang Yang, Hu Peng, Zhang Qi, Yu Fengwei, Wang Wei, Gu Shi
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Brecq:Pushing the limit of post-training quantization by block reconstruction
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) that comprise of spiking neurons to process …
Shikuang Deng, Shi Gu
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Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
Mixmix:All you need for data-free compression are feature and data mixing
User data confidentiality protection is becoming a rising challenge in the present deep learning research. Without access to data, …
Li Yuhang, Zhu Feng, Gong Ruihao, Shen Mingzhu, Dong Xin, Yu Fengwei, Lu Shaoqing, Gu Shi
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Mixmix:All you need for data-free compression are feature and data mixing