Aaron Nicolson
Signal Processing · 2019–2020
A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 51 citations into it; #4,803 of 7,572 by trust here, #6,083 by citations
- Artificial Intelligence — 39 citations into it
- Speech & Audio — 25 citations into it
- Cognitive Neuroscience — 17 citations into it; #6,378 of 10,852 by trust here, #9,453 by citations
- Evaluation & Benchmarks — 16 citations into it
- Computational Mechanics — 15 citations into it; #6,587 of 10,001 by trust here, #8,258 by citations
- Model Architectures — 14 citations into it
- Computer Vision and Pattern Recognition — 11 citations into it
Whom Aaron Nicolson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Rainer Martin
- Yong Xu
- Diederik P. Kingma
- Jimmy Ba
- Geoffrey E. Hinton
- Peter Vary
- Cees H. Taal
- Israel Cohen
- Kaiming He
- Richard Christian Hendriks
Who trusts Aaron Nicolson overall
Papers from Aaron Nicolson's perspective
Ordered by how strongly the researchers Aaron Nicolson 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Deep Residual-Dense Lattice Network for Speech Enhancement (2020) — 42 citations
- DeepMMSE: A Deep Learning Approach to MMSE-Based Noise Power Spectral Density Estimation (2020) — 131 citations
- Deep learning for minimum mean-square error approaches to speech enhancement (2019) — 128 citations