35 citations · 71 across the 6 of their papers we have counts for
6 papers
FedH2L: Federated Learning with Model and Statistical Heterogeneity
Yiying Li, Wei Zhou, Huaimin Wang +2
Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy. Mainstream FL approaches req…
Denoising convolutional autoencoder based B-mode ultrasound tongue image feature extraction
Bo Li, Kele Xu, Dawei Feng +3
B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the…
General audio tagging with ensembling convolutional neural network and statistical features
Kele Xu, Boqing Zhu, Qiuqiang Kong +4
Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solut…
Collaborative Deep Learning Across Multiple Data Centers
Kele Xu, Haibo Mi, Dawei Feng +4
Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter da…
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network
Dawei Feng, Kele Xu, Haibo Mi +2
Acoustic scene classification is an intricate problem for a machine. As an emerging field of research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this…
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network
Kele Xu, Dawei Feng, Haibo Mi +5
Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and f…