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Yonghong Tian, Yongjian Wu, Baochang Zhang, Yuxin Zhang, Rongrong Ji, Mingbao Lin - 2020
Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on ar...
Hsin-Min Wang, Yu Tsao, Yu-Huai Peng, Chen-Chou Lo, Hsin-Te Hwang, Hao Luo, Wen-Chin Huang - 2020
An effective approach for voice conversion (VC) is to disentangle linguistic content from other components in the speech signal. The effectiveness of variational autoencoder (VAE) based VC (VAE-VC), for instance, strongly relies on this principle. In our prior work, we proposed a cross-domain VAE-VC (CDVAE-VC) framework, which utilized acoustic f...
Yaohang Li, Luisa Velasco, Ryan Strauss, Michael Robertson, Evan Pritchard, Michelle P. Kuchera, W. Melnitchouk, Tianbo Liu, N. Sato, Yasir Alanazi - 2020
We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distribution...
Jimeng Sun, Lucas M. Glass, Wen Tang, Yasha Wang, Cao Xiao, Junyi Gao - 2020
Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1) a stage-aware long short-t...
Babak Shahbaba, Alexander N. Ngo, Dustin S. Pluta, Michelle N. Ngo - 2020
Biclustering is a class of techniques that simultaneously clusters the rows and columns of a matrix to sort heterogeneous data into homogeneous blocks. Although many algorithms have been proposed to find biclusters, existing methods suffer from the pre-specification of the number of biclusters or place constraints on the model structure. To addre...
Gerard de Melo, Guang Wang, Xin Dong, Yuting Wang, Yingqiang Ge, Shijie Geng, Yikun Xian, Zuohui Fu - 2020
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word...
Ian Reid, Lingqiao Liu, Ying Li, Bei Fang, Yu Liu, Haokui Zhang - 2020
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years. Being different from the RGB datasets, different HSI datasets are generally captured by various remote sensors and have different spectral configurations. Moreover, each HSI dataset only contains very limited training samples and...
Frank Weichert, Matthias Fey, Marian Kleineberg - 2020
This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations. While the deep generation of shapes has been mostly tackled by voxel and surface point cloud approaches, our generator learns to approximate the signed distance for any point in space given prior latent inform...
Chin-Hui Lee, Sabato Marco Siniscalchi, Chao-Han Huck Yang, Yannan Wang, Hu Hu, Jun Qi - 2020
We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to-vector regression formulation under a tensor-train network (TTN) framework. TTN is a recentl...
Simone Frintrop, Jianwei Zhang, Xiaolin Hu, Yulong Wang, Mikko Lauri, Ge Gao - 2020
This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D object from depth information represented by a point cloud. Deep features learned by convolutional neural networks from color information have been the dominant features to be used for inferring object poses, while depth information receives much less attentio...
