Aditya Arie Nugraha

Signal Processing · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 61 citations into it; #2,065 of 7,572 by trust here, #1,748 by citations
• Artificial Intelligence — 46 citations into it; #19,969 of 35,345 by trust here, #19,018 by citations
• Speech & Audio — 32 citations into it; #632 of 1,076 by trust here, #724 by citations
• Computational Mechanics — 14 citations into it; #4,630 of 10,001 by trust here, #6,877 by citations
• Optimization & Training — 10 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Natural Language Processing — 7 citations into it
• Cognitive Neuroscience — 6 citations into it

Whom Aditya Arie Nugraha trusts overall

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

1. Yoshua Bengio
2. Matthew D. Zeiler
3. Po-Sen Huang
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Tomas Mikolov
7. Arun Narayanan
8. Deliang Wang
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Aditya Arie Nugraha overall

1. Naoya Takahashi
2. Jen-Yu Liu
3. Yuki Mitsufuji
4. Shinji Watanabe
5. Daniel Stoller
6. Alexandre D'efossez
7. Laure Prétet
8. Yi-Hsuan Yang
9. Nabarun Goswami
10. Wei-Ning Hsu

Papers from Aditya Arie Nugraha's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017) — 401 citations
• Multichannel Audio Source Separation With Deep Neural Networks (2016) — 308 citations
• Multichannel music separation with deep neural networks (2016) — 87 citations