Xugang Lu
Artificial Intelligence · 2012–2021
A researcher in the Science corpus: 11 papers; trusts 111 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 110 citations into it; #5,466 of 35,345 by trust here, #6,517 by citations
- Signal Processing — 109 citations into it; #890 of 7,572 by trust here, #716 by citations
- Speech & Audio — 61 citations into it; #487 of 1,076 by trust here, #501 by citations
- Natural Language Processing — 28 citations into it
- Computer Vision and Pattern Recognition — 27 citations into it
- Computational Mechanics — 25 citations into it
- Cognitive Neuroscience — 20 citations into it; #6,881 of 10,852 by trust here, #4,528 by citations
- Model Architectures — 20 citations into it
Whom Xugang Lu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Graves
- Diederik P. Kingma
- Alex Krizhevsky
- Oriol Vinyals
- Yann LeCun
- Dong Yu
- Kaiming He
- George E. Dahl
Who trusts Xugang Lu overall
- Szu-Wei Fu
- Yi Luo
- Jinyu Li
- Shinji Watanabe
- Jonathan Le Roux
- Ziyue Zhao
- Takaaki Hori
- Daniel Michelsanti
- Deliang Wang
- Anurag Kumar
Papers from Xugang Lu's perspective
Ordered by how strongly the researchers Xugang Lu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Semi-supervised ensemble DNN acoustic model training (2017) — 19 citations
- MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement (2021) — 305 citations
- Speech enhancement based on deep denoising autoencoder (2013) — 851 citations
- Maximum a posteriori Based Decoding for CTC Acoustic Models (2016) — 44 citations
- Knowledge Distillation-Based Representation Learning for Short-Utterance Spoken Language Identification (2020) — 21 citations
- Interactive Learning of Teacher-student Model for Short Utterance Spoken Language Identification (2019) — 12 citations
- Feature Representation of Short Utterances Based on Knowledge Distillation for Spoken Language Identification (2018) — 24 citations
- End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks (2017) — 297 citations
- Raw waveform-based speech enhancement by fully convolutional networks (2017) — 211 citations
- SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement (2016) — 196 citations
- Factored recurrent neural network language model in TED lecture transcription (2012) — 12 citations