no code implementations • 7 Sep 2023 • Yu Du, Xu Liu, Yansong Chua
Speech enhancement seeks to extract clean speech from noisy signals.
no code implementations • 14 Mar 2023 • Tengjun Liu, Yansong Chua, Yiwei Zhang, Yuxiao Ning, Pengfu Liu, Guihua Wan, Zijun Wan, Shaomin Zhang, Weidong Chen
Despite its better bio-plausibility, goal-driven spiking neural network (SNN) has not achieved applicable performance for classifying biological spike trains, and showed little bio-functional similarities compared to traditional artificial neural networks.
no code implementations • 16 Feb 2023 • Pengfei Sun, Ehsan Eqlimi, Yansong Chua, Paul Devos, Dick Botteldooren
Spiking neural networks (SNN) are a promising research avenue for building accurate and efficient automatic speech recognition systems.
Ranked #2 on Audio Classification on SHD
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.
no code implementations • 14 Aug 2022 • Lei Jiang, Yongqing Liu, Shihai Xiao, Yansong Chua
Furthermore, we demonstrate analytically how lateral inhibition in artificial neural networks improves the quality of propagated gradients.
1 code implementation • 12 Jul 2021 • Timoleon Moraitis, Dmitry Toichkin, Adrien Journé, Yansong Chua, Qinghai Guo
All in all, Hebbian efficiency, theoretical underpinning, cross-entropy-minimization, and surprising empirical advantages, suggest that SoftHebb may inspire highly neuromorphic and radically different, but practical and advantageous learning algorithms and hardware accelerators.
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.
no code implementations • 28 Dec 2019 • Yukuan Yang, Lei Deng, Peng Jiao, Yansong Chua, Jing Pei, Cheng Ma, Guoqi Li
In summary, this work provides a new solution for lensless imaging through scattering media using transfer learning in DNNs.
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 • 3 Apr 2019 • Roshan Gopalakrishnan, Yansong Chua, Ashish Jith Sreejith Kumar
The hardware-software co-optimization of neural network architectures is becoming a major stream of research especially due to the emergence of commercial neuromorphic chips such as the IBM Truenorth and Intel Loihi.
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.
no code implementations • 1 Jan 2019 • Roshan Gopalakrishnan, Ashish Jith Sreejith Kumar, Yansong Chua
Neuromorphic systems or dedicated hardware for neuromorphic computing is getting popular with the advancement in research on different device materials for synapses, especially in crossbar architecture and also algorithms specific or compatible to neuromorphic hardware.
no code implementations • 3 Jul 2018 • Laxmi R. Iyer, Yansong Chua, Haizhou Li
We also use this SNN for further experiments on N-MNIST to show that rate based SNNs perform better, and precise spike timings are not important in N-MNIST.
no code implementations • 2 Jul 2018 • Roshan Gopalakrishnan, Yansong Chua, Laxmi R. Iyer
Since then, several neuromorphic datasets as obtained by applying such sensors on image datasets (e. g. the neuromorphic CALTECH 101) have been introduced.