Martin Wistuba

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 16 papers; trusts 1,317 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 429 citations into it; #5,452 of 35,345 by trust here, #7,728 by citations
• Computer Vision and Pattern Recognition — 255 citations into it; #7,045 of 21,575 by trust here, #10,500 by citations
• Safety, Robustness & Interpretability — 93 citations into it
• Model Architectures — 85 citations into it
• Optimization & Training — 84 citations into it
• Meta & Continual Learning — 66 citations into it
• Neural Architecture Search & AutoML — 66 citations into it; #218 of 283 by trust here, #250 by citations
• Representation & Self-Supervised Learning — 49 citations into it

Whom Martin Wistuba trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Barret Zoph
5. Yann LeCun
6. Kaiming He
7. James Bergstra
8. Quoc V. Le
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Martin Wistuba overall

1. Marc-André Zöller
2. Marco F. Huber
3. Tejaswini Pedapati
4. Matthias Feurer
5. Gang Luo
6. Ambrish Rawat
7. Nicolas Schilling
8. Katharina Eggensperger
9. Frank Hutter
10. Jan N. van Rijn

Papers from Martin Wistuba's perspective

Ordered by how strongly the researchers Martin Wistuba 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Adversarial Phenomenon in the Eyes of Bayesian Deep Learning (2017) — 42 citations
• Automatic Frankensteining: Creating Complex Ensembles Autonomously (2017) — 33 citations
• Learning hyperparameter optimization initializations (2015) — 108 citations
• Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization (2015) — 78 citations
• Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2017) — 150 citations
• Adversarial Robustness Toolbox v1.0.0 (2018) — 573 citations
• Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutations (2018) — 49 citations
• Inductive Transfer for Neural Architecture Optimization (2019) — 9 citations
• Practical Deep Learning Architecture Optimization (2017) — 21 citations
• Memory Efficient Continual Learning with Transformers (2022) — 70 citations
• Continual Learning with Transformers for Image Classification (2022) — 27 citations
• Memory Efficient Continual Learning for Neural Text Classification (2022) — 5 citations
• A Survey on Neural Architecture Search (2019) — 293 citations
• Adversarial Robustness Toolbox v0.2.2 (2018) — 59 citations
• Personalized Deep Learning for Tag Recommendation (2017) — 66 citations
• Learning time-series shapelets (2014) — 491 citations