Martin Mundt

Artificial Intelligence · 2012–2021

A researcher in the Science corpus: 5 papers; trusts 511 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 229 citations into it; #19,414 of 35,345 by trust here, #12,244 by citations
• Computer Vision and Pattern Recognition — 167 citations into it; #18,946 of 21,575 by trust here, #12,992 by citations
• Meta & Continual Learning — 71 citations into it
• Representation & Self-Supervised Learning — 60 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Optimization & Training — 30 citations into it
• Generative Models — 29 citations into it
• Reinforcement Learning — 23 citations into it

Whom Martin Mundt trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yann LeCun
5. Kaiming He
6. Yoshua Bengio
7. Alex Graves
8. L. Eon Bottou
9. Ilya Sutskever
10. Max Welling

Who trusts Martin Mundt overall

1. Jorge Armando Mendez Mendez
2. Rui Gao
3. Timothée Lesort
4. Oleksiy Ostapenko
5. Steven Braun
6. Yujun Shi
7. Sam Powers
8. Ali Ayub
9. Gido M. van de Ven
10. Ying Zhao

Papers from Martin Mundt's perspective

Ordered by how strongly the researchers Martin Mundt trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Avalanche: an End-to-End Library for Continual Learning (2021) — 219 citations
• A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning (2020) — 183 citations
• CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability (2021) — 42 citations
• Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition (2019) — 43 citations
• CMOS Integrated Antenna-Coupled Field-Effect Transistors for the Detection of Radiation From 0.2 to 4.3 THz (2012) — 207 citations