26 citations · 94 across the 20 of their papers we have counts for
20 papers
SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
Shuaijie Shen, Chao Wang, Renzhuo Huang +5
Known as low energy consumption networks, spiking neural networks (SNNs) have gained a lot of attention within the past decades. While SNNs are increasing competitive with artifici…
Learning Visual Conditioning Tokens to Correct Domain Shift for Fully Test-time Adaptation
Yushun Tang, Shuoshuo Chen, Zhehan Kan +3
Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradat…
BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation
Zekai Xu, Kang You, Qinghai Guo +2
Spiking neural networks (SNNs), which mimic biological neural system to convey information via discrete spikes, are well known as brain-inspired models with excellent computing eff…
Benchmarking Neural Decoding Backbones towards Enhanced On-edge iBCI Applications
Zhou Zhou, Guohang He, Zheng Zhang +5
Traditional invasive Brain-Computer Interfaces (iBCIs) typically depend on neural decoding processes conducted on workstations within laboratory settings, which prevents their ever…
SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN
Kang You, Zekai Xu, Chen Nie +4
Spiking neural network (SNN) has attracted great attention due to its characteristic of high efficiency and accuracy. Currently, the ANN-to-SNN conversion methods can obtain ANN on…
Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling
Guiqin Wang, Peng Zhao, Cong Zhao +5
Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple insta…
Automotive Object Detection via Learning Sparse Events by Spiking Neurons
Hu Zhang, Yanchen Li, Luziwei Leng +5
Event-based sensors, distinguished by their high temporal resolution of 1 and a dynamic range of 120 , stand out as ideal tools for deployment in fast…
Cross-Inferential Networks for Source-free Unsupervised Domain Adaptation
Yushun Tang, Qinghai Guo, Zhihai He
One central challenge in source-free unsupervised domain adaptation (UDA) is the lack of an effective approach to evaluate the prediction results of the adapted network model in th…
Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation
Umais Zahid, Qinghai Guo, Zafeirios Fountas
Backpropagation has rapidly become the workhorse credit assignment algorithm for modern deep learning methods. Recently, modified forms of predictive coding (PC), an algorithm with…
Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network
Rui Zhang, Luziwei Leng, Kaiwei Che +5
Spiking neural networks (SNNs), known for their low-power, event-driven computation and intrinsic temporal dynamics, are emerging as promising solutions for processing dynamic, asy…
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
Yushun Tang, Ce Zhang, Heng Xu +5
Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradat…
Curvature-Sensitive Predictive Coding with Approximate Laplace Monte Carlo
Umais Zahid, Qinghai Guo, Karl Friston +1
Predictive coding (PC) accounts of perception now form one of the dominant computational theories of the brain, where they prescribe a general algorithm for inference and learning…
Self-Supervised Learning Through Efference Copies
Franz Scherr, Qinghai Guo, Timoleon Moraitis
Self-supervised learning (SSL) methods aim to exploit the abundance of unlabelled data for machine learning (ML), however the underlying principles are often method-specific. An SS…
Hebbian Deep Learning Without Feedback
Adrien Journé, Hector Garcia Rodriguez, Qinghai Guo +1
Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. M…
Modelling non-reinforced preferences using selective attention
Noor Sajid, Panagiotis Tigas, Zafeirios Fountas +3
How can artificial agents learn non-reinforced preferences to continuously adapt their behaviour to a changing environment? We decompose this question into two challenges: () en…
Short-Term Plasticity Neurons Learning to Learn and Forget
Hector Garcia Rodriguez, Qinghai Guo, Timoleon Moraitis
Short-term plasticity (STP) is a mechanism that stores decaying memories in synapses of the cerebral cortex. In computing practice, STP has been used, but mostly in the niche of sp…
Variational Predictive Routing with Nested Subjective Timescales
Alexey Zakharov, Qinghai Guo, Zafeirios Fountas
Discovery and learning of an underlying spatiotemporal hierarchy in sequential data is an important topic for machine learning. Despite this, little work has been done to explore h…
Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural Networks
Alan Jeffares, Qinghai Guo, Pontus Stenetorp +1
Biological spiking neural networks (SNNs) can temporally encode information in their outputs, e.g. in the rank order in which neurons fire, whereas artificial neural networks (ANNs…
SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All Networks
Timoleon Moraitis, Dmitry Toichkin, Adrien Journé +2
Hebbian plasticity in winner-take-all (WTA) networks is highly attractive for neuromorphic on-chip learning, owing to its efficient, local, unsupervised, and on-line nature. Moreov…
Multiscale Change-point Segmentation: Beyond Step Functions
Housen Li, Qinghai Guo, Axel Munk
Modern multiscale type segmentation methods are known to detect multiple change-points with high statistical accuracy, while allowing for fast computation. Underpinning theory has…