Teresa B Ludermir

Artificial Intelligence · 2004–2019

A researcher in the Science corpus: 5 papers; trusts 26 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #11,277 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #9,916 of 21,575 by trust here, #18,266 by citations
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Statistics and Probability — 5 citations into it

Whom Teresa B Ludermir trusts overall

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

1. Spyros Makridakis
2. David E. Rumelhart
3. Pavel Brazdil
4. Geoffrey E. Hinton
5. M. Hibon
6. Kaiming He
7. Ronald J. Williams
8. Alex Smola
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Teresa B Ludermir overall

1. Christiane Lemke
2. Bogdan Gabrys
3. Frank Hutter
4. Katharina Eggensperger
5. Radwa El Shawi
6. Tong Yu
7. Iván Olier

Papers from Teresa B Ludermir's perspective

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

Their papers

• Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors (2008) — 33 citations
• Selection of time series forecasting models based on performance information (2004) — 20 citations
• Using Machine Learning Techniques to Combine Forecasting Methods (2004) — 37 citations
• Isotropic Maximization Loss and Entropic Score: Fast, Accurate, Scalable, Unexposed, Turnkey, and Native Neural Networks Out-of-Distribution Detection. (2019) — 3 citations
• Distinction Maximization Loss: Fast, Scalable, Turnkey, and Native Neural Networks Out-of-Distribution Detection simply by Replacing the SoftMax Loss (2019) — 4 citations