Nils Y. Hammerla

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 48 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #8,112 of 35,345 by trust here, #5,638 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #7,239 of 21,575 by trust here, #4,807 by citations
• Natural Language Processing — 23 citations into it; #1,866 of 5,688 by trust here, #2,605 by citations
• Model Architectures — 19 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Biomedical Engineering — 10 citations into it
• Materials Chemistry — 7 citations into it
• Signal Processing — 7 citations into it

Whom Nils Y. Hammerla trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Yann LeCun
7. Ronan Collobert
8. Nitish Srivastava
9. Sepp Hochreiter
10. J. Urgen Schmidhuber

Who trusts Nils Y. Hammerla overall

1. Yu Guan
2. Sreenivasan Ramasamy Ramamurthy
3. Dalin Zhang
4. Thomas Plötz
5. Lina Yao
6. Nidhi Dua
7. Kun Xia
8. Xiaochen Zheng
9. Song-Mi Lee
10. Florenc Demrozi

Papers from Nils Y. Hammerla's perspective

Ordered by how strongly the researchers Nils Y. Hammerla 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Offline bilingual word vectors, orthogonal transformations and the inverted softmax (2017) — 559 citations
• Attention U-Net: Learning Where to Look for the Pancreas (2018) — 7,627 citations
• Relational Graph Attention Networks (2018) — 247 citations
• Explaining Deep Learning Models with Constrained Adversarial Examples (2019) — 45 citations
• Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study (2017) — 1,139 citations
• Sorting out symptoms: design and evaluation of the 'babylon check' automated triage system (2016) — 34 citations
• Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables (2016) — 996 citations
• PD Disease State Assessment in Naturalistic Environments Using Deep Learning (2015) — 193 citations