Search Results for author:
Found 36 papers, 11 papers with code
Binary Event-Driven Spiking Transformer
In this paper, we integrate binarization techniques into Transformer-based SNNs and propose the Binary Event-Driven Spiking Transformer, i. e. BESTformer.
A Compressive Memory-based Retrieval Approach for Event Argument Extraction
Recent works have demonstrated the effectiveness of retrieval augmentation in the Event Argument Extraction (EAE) task.
Raindrop Clarity: A Dual-Focused Dataset for Day and Night Raindrop Removal
There are 5, 442 daytime raindrop images and 9, 744 nighttime raindrop images.
Ternary Spike-based Neuromorphic Signal Processing System
QT-SNN, compatible with ternary spike trains from the TAE method, quantifies both membrane potentials and synaptic weights to reduce memory requirements while maintaining performance.
Q-SNNs: Quantized Spiking Neural Networks
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, offering an energy-efficient paradigm for the next generation of machine intelligence.
Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting
Here, we take advantage of spiking neural networks' energy efficiency and propose an end-to-end lightweight KWS model.
SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning
However, existing spiking federated learning methods employ a random selection approach for client aggregation, assuming unbiased client participation.
Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning
To address this issue, we have designed a Spiking Multiscale Attention (SMA) module that captures multiscale spatiotemporal interaction information.
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events.
Event-Driven Learning for Spiking Neural Networks
Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms.
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices.
CoAVT: A Cognition-Inspired Unified Audio-Visual-Text Pre-Training Model for Multimodal Processing
To bridge the gap between modalities, CoAVT employs a query encoder, which contains a set of learnable query embeddings, and extracts the most informative audiovisual features of the corresponding text.
A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators
Yet, existing works on utilizing LLMs for automatic dialogue evaluation are limited in their scope in terms of the number of meta-evaluation datasets, mode of evaluation, coverage of LLMs, etc.
JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement
By treating Retinex- and semantic-based priors as the condition, JoReS-Diff presents a unique perspective for establishing an diffusion model for LLIE and similar image enhancement tasks.
SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection
As the demand for heightened performance in SNNs surges, the trend towards training deeper networks becomes imperative, while residual learning stands as a pivotal method for training deep neural networks.
Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate
Recurrent Neural Networks (RNNs) are widely recognized for their proficiency in modeling temporal dependencies, making them highly prevalent in sequential data processing applications.
Tensor Decomposition Based Attention Module for Spiking Neural Networks
Then, in AMC, we exploit the inverse procedure of the tensor decomposition process to combine the three tensors into the attention map using a so-called connecting factor.
ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling
In this paper, we propose an efficient spiking variational autoencoder (ESVAE) that constructs an interpretable latent space distribution and design a reparameterizable spiking sampling method.
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs.
Rethinking Relation Classification with Graph Meaning Representations
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research.
Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction.
Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks
Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing.
Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert
To address the problem, we propose using a lip-reading expert to improve the intelligibility of the generated lip regions by penalizing the incorrect generation results.
Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation
As a temporal encoding scheme for SNNs, Time-To-First-Spike (TTFS) encodes information using the timing of a single spike, which allows spiking neurons to transmit information through sparse spike trains and results in lower power consumption and higher computational efficiency compared to traditional rate-based encoding counterparts.
Training Spiking Neural Networks with Local Tandem Learning
The LTL rule follows the teacher-student learning approach by mimicking the intermediate feature representations of a pre-trained ANN.
TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks
Given the critical role of attention mechanisms in enhancing neural network performance, the integration of SNNs and attention mechanisms exhibits potential to deliver energy-efficient and high-performance computing paradigms.
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction.
Sequential Random Network for Fine-grained Image Classification
To address this issue, we use the proposed SRN which composed of BiLSTM and several Tanh-Dropout blocks (called BiLSTM-TDN), to further process DCNN one-dimensional features for highlighting the detail information of image.
Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network
Inspired by the mammal's auditory localization pathway, in this paper we propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment, and implement this algorithm in a real-time robotic system with a microphone array.
You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy
Rate-encoded SNNs could be seen as inefficient as an encoding scheme because it involves the transmission of a large number of spikes.
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
In STDBP algorithm, the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner.
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Neural Population Coding for Effective Temporal Classification
We show that, with population neural codings, the encoded patterns are linearly separable using the Support Vector Machine (SVM).
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks
The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve.
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.
Deep Spiking Neural Network with Spike Count based Learning Rule
Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synchronous analog counterpart.
