Benedikt Pfülb

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 239 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #9,606 of 35,345 by trust here, #15,866 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #6,719 of 21,575 by trust here, #10,677 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Meta & Continual Learning — 15 citations into it; #277 of 1,041 by trust here, #568 by citations
• Optimization & Training — 13 citations into it
• Generative Models — 9 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Benedikt Pfülb trusts overall

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

1. Alex Krizhevsky
2. Alexander Rainer Tassilo Gepperth
3. Geoffrey E. Hinton
4. Yann LeCun
5. Diederik P. Kingma
6. Yoshua Bengio
7. Ilya Sutskever
8. Aäron van den Oord
9. Marc'Aurelio Ranzato
10. Ronald Kemker

Who trusts Benedikt Pfülb overall

1. Marc Masana
2. Alexander Rainer Tassilo Gepperth
3. Martin Mundt
4. Magdalena Biesialska
5. Benedikt Bagus
6. Haeyong Kang
7. Liyuan Wang
8. Tinne Tuytelaars
9. Joost van de Weijer
10. Hanbin Zhao

Papers from Benedikt Pfülb's perspective

Ordered by how strongly the researchers Benedikt Pfülb 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. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A comprehensive, application-oriented study of catastrophic forgetting in DNNs (2019) — 95 citations
• Continual Learning with Fully Probabilistic Models (2021) — 10 citations
• Image Modeling with Deep Convolutional Gaussian Mixture Models (2021) — 9 citations
• Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces (2019) — 4 citations