Daniel Michelsanti

Signal Processing · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 191 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 74 citations into it; #2,469 of 7,572 by trust here, #3,099 by citations
• Artificial Intelligence — 53 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it; #8,599 of 21,575 by trust here, #12,992 by citations
• Speech & Audio — 37 citations into it
• Cognitive Neuroscience — 21 citations into it
• Model Architectures — 21 citations into it
• Natural Language Processing — 17 citations into it
• Computational Mechanics — 16 citations into it

Whom Daniel Michelsanti trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ian J. Goodfellow
5. Andrew Zisserman
6. Ilya Sutskever
7. Alex Krizhevsky
8. P. Loizou
9. Yann LeCun
10. Alex Graves

Who trusts Daniel Michelsanti overall

1. Chris Donahue
2. Andrew Owens
3. Ori Ernst
4. Berrak Sisman
5. Ke Wang
6. Zheng-Hua Tan
7. Alexei A. Efros
8. Bo Li
9. Mira Lilleholt Vik
10. Kateřina Žmolíková

Papers from Daniel Michelsanti's perspective

Ordered by how strongly the researchers Daniel Michelsanti 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. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification (2017) — 224 citations
• An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation (2020) — 337 citations