Emre Çakir

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 32 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #24,069 of 35,345 by trust here, #24,417 by citations
• Signal Processing — 25 citations into it; #2,338 of 7,572 by trust here, #2,221 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #16,008 of 21,575 by trust here, #13,542 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Emre Çakir trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Xavier Glorot
8. Karen Simonyan
9. Yann LeCun
10. Sergey Ioffe

Who trusts Emre Çakir overall

1. Dan Stowell
2. Tuomas Virtanen
3. Annamaria Mesaros
4. Eduardo Fonseca
5. Justin Salamon
6. Hendrik Purwins
7. Toni Heittola
8. Hyunsin Park
9. Bongjun Kim
10. Giambattista Parascandolo

Papers from Emre Çakir's perspective

Ordered by how strongly the researchers Emre Çakir 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Unsupervised adversarial domain adaptation for acoustic scene classification (2018) — 46 citations
• Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection (2017) — 616 citations
• Filterbank learning for deep neural network based polyphonic sound event detection (2016) — 36 citations
• Polyphonic sound event detection using multi label deep neural networks (2015) — 298 citations