Barbara Hammer

Artificial Intelligence · 2001–2018

A researcher in the Science corpus: 13 papers; trusts 94 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it; #5,956 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #8,469 of 21,575 by trust here, #15,529 by citations
• Statistical Learning Theory — 22 citations into it; #797 of 1,038 by trust here, #920 by citations
• Optimization & Training — 19 citations into it
• Model Architectures — 14 citations into it
• Management Science and Operations Research — 12 citations into it; #6,757 of 11,946 by trust here, #8,469 by citations
• Statistics and Probability — 11 citations into it
• Meta & Continual Learning — 10 citations into it; #682 of 1,041 by trust here, #963 by citations

Whom Barbara Hammer trusts overall

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

1. Risi Kondor
2. Thomas Gärtner
3. Trevor J. Hastie
4. Gregory Ditzler
5. Radford M. Neal
6. Alessandro Sperduti
7. R. M. French
8. L. Eon Bottou
9. Geoffrey E. Hinton
10. Jason Aaron Edward Weston

Who trusts Barbara Hammer overall

1. German Ignacio Parisi
2. Michael Biehl
3. Thomas Villmann
4. Andreas Backhaus
5. Timothée Lesort
6. Petra Schneider
7. Henrik Jacobsson
8. Stefan Wermter
9. Robin Manhaeve
10. Werner Uwents

Papers from Barbara Hammer's perspective

Ordered by how strongly the researchers Barbara Hammer 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• Incremental on-line learning: A review and comparison of state of the art algorithms (2018) — 353 citations
• Mitigating Concept Drift via Rejection (2018) — 16 citations
• Prototype-based models in machine learning. (2016) — 185 citations
• Incremental learning algorithms and applications (2016) — 333 citations
• Brain-Inspired Computing, International Workshop, Cetraro/Italy, July 2013 (2014)
• Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar] (2009) — 30 citations
• Adaptive Relevance Matrices in Learning Vector Quantization (2009) — 415 citations
• Perspectives of Neural-Symbolic Integration (2007) — 97 citations
• Merge SOM for temporal data (2005) — 132 citations
• Neural methods for non-standard data (2004) — 36 citations
• Generalized relevance learning vector quantization (2002) — 473 citations
• Architectural Bias in Recurrent Neural Networks: Fractal Analysis (2002) — 36 citations
• Neural Smithing – Supervised Learning in Feedforward Artificial Neural Networks (2001) — 368 citations