no code implementations • 1 Mar 2024 • Wenjie Wei, Malu Zhang, Jilin Zhang, Ammar Belatreche, Jibin Wu, Zijing Xu, Xuerui Qiu, Hong Chen, Yang Yang, Haizhou Li
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.
no code implementations • 26 Jan 2024 • Qianhui Liu, Jiaqi Yan, Malu Zhang, Gang Pan, Haizhou Li
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.
no code implementations • 22 Jan 2024 • Xianghu Yue, Xiaohai Tian, Lu Lu, Malu Zhang, Zhizheng Wu, Haizhou Li
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.
1 code implementation • 24 Dec 2023 • Chen Zhang, Luis Fernando D'Haro, Yiming Chen, Malu Zhang, Haizhou Li
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.
no code implementations • 23 Oct 2023 • Qu Yang, Malu Zhang, Jibin Wu, Kay Chen Tan, Haizhou Li
With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs.
1 code implementation • 23 Oct 2023 • Haoyu Deng, Ruijie Zhu, Xuerui Qiu, Yule Duan, Malu Zhang, LiangJian Deng
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.
no code implementations • 23 Oct 2023 • Pengfei Sun, Jibin Wu, Malu Zhang, Paul Devos, Dick Botteldooren
Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing.
1 code implementation • 23 Oct 2023 • Qiugang Zhan, Xiurui Xie, Guisong Liu, Malu Zhang
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.
no code implementations • 15 Oct 2023 • Li Zhou, Wenyu Chen, Dingyi Zeng, Malu Zhang, Daniel Hershcovich
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research.
no code implementations • 8 Oct 2023 • Wanlong Liu, Dingyi Zeng, Li Zhou, Yichen Xiao, Weishan Kong, Malu Zhang, Shaohuan Cheng, Hongyang Zhao, Wenyu Chen
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction.
no code implementations • 18 Sep 2023 • Zeyang Song, Jibin Wu, Malu Zhang, Mike Zheng Shou, Haizhou Li
Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing.
1 code implementation • CVPR 2023 • Jiadong Wang, Xinyuan Qian, Malu Zhang, Robby T. Tan, Haizhou Li
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.
no code implementations • ICCV 2023 • Wenjie Wei, Malu Zhang, Hong Qu, Ammar Belatreche, Jian Zhang, Hong Chen
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.
1 code implementation • 10 Oct 2022 • Qu Yang, Jibin Wu, Malu Zhang, Yansong Chua, Xinchao Wang, Haizhou Li
The LTL rule follows the teacher-student learning approach by mimicking the intermediate feature representations of a pre-trained ANN.
1 code implementation • 21 Jun 2022 • Rui-Jie Zhu, Malu Zhang, Qihang Zhao, Haoyu Deng, Yule Duan, Liang-Jian Deng
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.
no code implementations • 15 Oct 2021 • Li Zhou, Wenyu Chen, Dingyi Zeng, Shaohuan Cheng, Wanlong Liu, Malu Zhang, Hong Qu
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.
no code implementations • 12 Mar 2021 • Chaorong Li, Malu Zhang, Wei Huang, Fengqing Qin, Anping Zeng, Yuanyuan Huang
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.
no code implementations • 7 Jul 2020 • Zihan Pan, Malu Zhang, Jibin Wu, Haizhou Li
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.
no code implementations • 3 Jun 2020 • Srivatsa P, Kyle Timothy Ng Chu, Burin Amornpaisannon, Yaswanth Tavva, Venkata Pavan Kumar Miriyala, Jibin Wu, Malu Zhang, Haizhou Li, Trevor E. Carlson
Rate-encoded SNNs could be seen as inefficient as an encoding scheme because it involves the transmission of a large number of spikes.
no code implementations • 26 Mar 2020 • Malu Zhang, Jiadong Wang, Burin Amornpaisannon, Zhixuan Zhang, VPK Miriyala, Ammar Belatreche, Hong Qu, Jibin Wu, Yansong Chua, Trevor E. Carlson, Haizhou Li
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.
1 code implementation • 19 Nov 2019 • Jibin Wu, Emre Yilmaz, Malu Zhang, Haizhou Li, Kay Chen Tan
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
no code implementations • 12 Sep 2019 • Zihan Pan, Jibin Wu, Yansong Chua, Malu Zhang, Haizhou Li
We show that, with population neural codings, the encoded patterns are linearly separable using the Support Vector Machine (SVM).
no code implementations • 3 Sep 2019 • Zihan Pan, Yansong Chua, Jibin Wu, Malu Zhang, Haizhou Li, Eliathamby Ambikairajah
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.
1 code implementation • 2 Jul 2019 • Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, Kay Chen Tan
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.
no code implementations • 15 Feb 2019 • Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li, Haizhou Li
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.