304 citations · 1k across the 30 of their papers we have counts for
9 papers · 1 filter
A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
Yunzhe Hao, Xuhui Huang, Meng Dong +1
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are n…
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks
Jing Shi, Jiaming Xu, Yiqun Yao +1
Deep neural networks have shown superior performance in many regimes to remember familiar patterns with large amounts of data. However, the standard supervised deep learning paradi…
Cascaded Mutual Modulation for Visual Reasoning
Yiqun Yao, Jiaming Xu, Feng Wang +1
Visual reasoning is a special visual question answering problem that is multi-step and compositional by nature, and also requires intensive text-vision interactions. We propose CMM…
Single-channel Speech Dereverberation via Generative Adversarial Training
Chenxing Li, Tieqiang Wang, Shuang Xu +1
In this paper, we propose a single-channel speech dereverberation system (DeReGAT) based on convolutional, bidirectional long short-term memory and deep feed-forward neural network…
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin
Linhao Dong, Shiyu Zhou, Wei Chen +1
End-to-end models have been showing superiority in Automatic Speech Recognition (ASR). At the same time, the capacity of streaming recognition has become a growing requirement for…
NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition
Fenfen Sheng, Zhineng Chen, Bo Xu
Scene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks.…
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese
Shiyu Zhou, Linhao Dong, Shuang Xu +1
The choice of modeling units is critical to automatic speech recognition (ASR) tasks. Conventional ASR systems typically choose context-dependent states (CD-states) or context-depe…
Unsupervised Neural Machine Translation with Weight Sharing
Zhen Yang, Wei Chen, Feng Wang +1
Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models propo…
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese
Shiyu Zhou, Linhao Dong, Shuang Xu +1
Sequence-to-sequence attention-based models have recently shown very promising results on automatic speech recognition (ASR) tasks, which integrate an acoustic, pronunciation and l…