Ashutosh Pandey

Signal Processing · with Wang · 2019

A researcher in the Science corpus: 2 papers; trusts 60 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 34 citations into it; #1,730 of 7,572 by trust here, #4,900 by citations
• Artificial Intelligence — 21 citations into it; #25,272 of 35,345 by trust here, #34,213 by citations
• Speech & Audio — 15 citations into it
• Cognitive Neuroscience — 12 citations into it
• Computational Mechanics — 12 citations into it; #2,283 of 10,001 by trust here, #6,287 by citations
• Computer Vision and Pattern Recognition — 9 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 6 citations into it

Whom Ashutosh Pandey trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Yuxuan Wang
3. Deliang Wang
4. Diederik P. Kingma
5. X. Zhang
6. Yoshua Bengio
7. Sergey Ioffe
8. Santiago Pascual
9. Geoffrey E. Hinton
10. Jitong Chen

Who trusts Ashutosh Pandey overall

1. Yuzhou Liu
2. Xue Jiang
3. Xiulian Peng
4. Deliang Wang
5. Chengyu Zheng
6. Xiang Hao
7. Jun Chen
8. Yan Lu
9. Huaying Xue
10. Xiangdong Su

Papers from Ashutosh Pandey's perspective

Ordered by how strongly the researchers Ashutosh Pandey trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• TCNN: Temporal Convolutional Neural Network for Real-time Speech Enhancement in the Time Domain (2019) — 382 citations
• A New Framework for CNN-Based Speech Enhancement in the Time Domain (2019) — 261 citations