Jonas W. Mueller

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 10 papers; trusts 477 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 219 citations into it; #6,755 of 35,345 by trust here, #4,923 by citations
• Computer Vision and Pattern Recognition — 142 citations into it; #5,602 of 21,575 by trust here, #4,834 by citations
• Natural Language Processing — 57 citations into it; #1,627 of 5,688 by trust here, #2,808 by citations
• Reinforcement Learning — 52 citations into it
• Model Architectures — 44 citations into it
• Optimization & Training — 38 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Representation & Self-Supervised Learning — 28 citations into it; #955 of 3,145 by trust here, #1,885 by citations

Whom Jonas W. Mueller trusts overall

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

1. Geoffrey E. Hinton
2. Ilya Sutskever
3. Diederik P. Kingma
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Tomas Mikolov
8. Alex Graves
9. Kaiming He
10. Oriol Vinyals

Who trusts Jonas W. Mueller overall

1. Juri Opitz
2. Zhiquan Ye
3. Vukosi Marivate
4. Mathijs Mul
5. Mahmut Kaya
6. Shervin Minaee
7. Zhiqiang Hu
8. Hao Wang
9. Tong Niu
10. Dallas Card

Papers from Jonas W. Mueller's perspective

Ordered by how strongly the researchers Jonas W. Mueller 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• ResNeSt: Split-Attention Networks (2020) — 1,747 citations
• Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks (2021) — 711 citations
• Siamese Recurrent Architectures for Learning Sentence Similarity (2016) — 992 citations
• IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation (2019) — 77 citations
• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020) — 995 citations
• Antibody complementarity determining region design using high-capacity machine learning (2019) — 172 citations
• Principal Differences Analysis: Interpretable Characterization of Differences between Distributions (2015) — 39 citations
• Continuous Doubly Constrained Batch Reinforcement Learning (2021) — 36 citations
• Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation (2020) — 63 citations
• Sequence to Better Sequence: Continuous Revision of Combinatorial Structures (2017) — 100 citations