Alexander Rainer Tassilo Gepperth

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 11 papers; trusts 608 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 170 citations into it; #3,979 of 35,345 by trust here, #7,112 by citations
• Computer Vision and Pattern Recognition — 137 citations into it; #6,374 of 21,575 by trust here, #8,296 by citations
• Meta & Continual Learning — 61 citations into it; #85 of 1,041 by trust here, #188 by citations
• Representation & Self-Supervised Learning — 46 citations into it
• Optimization & Training — 38 citations into it
• Model Architectures — 32 citations into it
• Reinforcement Learning — 21 citations into it
• Generative Models — 18 citations into it

Whom Alexander Rainer Tassilo Gepperth trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yann LeCun
5. Yoshua Bengio
6. R. M. French
7. Ilya Sutskever
8. Ian J. Goodfellow
9. Radford M. Neal
10. Aäron van den Oord

Who trusts Alexander Rainer Tassilo Gepperth overall

1. Benedikt Pfülb
2. Timothée Lesort
3. Tyler L. Hayes
4. James Smith
5. Rahaf Aljundi
6. German Ignacio Parisi
7. Ronald Kemker
8. Marc Masana
9. Martin Mundt
10. Hichem Sahbi

Papers from Alexander Rainer Tassilo Gepperth's perspective

Ordered by how strongly the researchers Alexander Rainer Tassilo Gepperth 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. 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

• A comprehensive, application-oriented study of catastrophic forgetting in DNNs (2019) — 95 citations
• Marginal Replay vs Conditional Replay for Continual Learning (2018) — 38 citations
• A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems (2016) — 160 citations
• Incremental learning with self-organizing maps (2017) — 11 citations
• Continual Learning with Fully Probabilistic Models (2021) — 10 citations
• Image Modeling with Deep Convolutional Gaussian Mixture Models (2021) — 9 citations
• An Investigation of Replay-based Approaches for Continual Learning (2021) — 48 citations
• Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural Networks (2019) — 5 citations
• Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces (2019) — 4 citations
• A Study on Catastrophic Forgetting in Deep LSTM Networks (2019) — 35 citations
• Incremental learning algorithms and applications (2016) — 333 citations