Heikki Huttunen

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 5 papers; trusts 47 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #24,070 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #9,834 of 21,575 by trust here, #10,677 by citations
• Signal Processing — 22 citations into it; #1,815 of 7,572 by trust here, #1,820 by citations
• Model Architectures — 21 citations into it
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Natural Language Processing — 12 citations into it
• Statistics and Probability — 11 citations into it

Whom Heikki Huttunen trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Yoshua Bengio
5. Sepp Hochreiter
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Kaiming He
9. Annamaria Mesaros
10. Mike Schuster

Who trusts Heikki Huttunen overall

1. Justin Salamon
2. Annamaria Mesaros
3. Tuomas Virtanen
4. Toni Heittola
5. Dan Stowell
6. Hendrik Purwins
7. Juan Pablo Bello
8. Yapeng Tian
9. Eduardo Fonseca
10. Emre Çakir

Papers from Heikki Huttunen's perspective

Ordered by how strongly the researchers Heikki Huttunen 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Recurrent neural networks for polyphonic sound event detection in real life recordings (2016) — 337 citations
• HARK Side of Deep Learning - From Grad Student Descent to Automated Machine Learning (2019) — 38 citations
• Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection (2017) — 616 citations
• Polyphonic sound event detection using multi label deep neural networks (2015) — 298 citations
• Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014) — 694 citations