Stefan L. Frank
Artificial Intelligence · 2006–2019
A researcher in the Science corpus: 3 papers; trusts 68 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 25 citations into it; #9,349 of 35,345 by trust here, #19,018 by citations
- Natural Language Processing — 19 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it; #15,753 of 21,575 by trust here, #18,266 by citations
- Cognitive Neuroscience — 12 citations into it; #10,299 of 10,852 by trust here, #8,363 by citations
- Developmental and Educational Psychology — 12 citations into it; #7,822 of 8,223 by trust here, #5,471 by citations
- Multimodal Learning — 8 citations into it; #835 of 1,211 by trust here, #1,136 by citations
- Representation & Self-Supervised Learning — 7 citations into it; #1,275 of 3,145 by trust here, #3,009 by citations
- Signal Processing — 6 citations into it
Whom Stefan L. Frank trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jeffrey L. Elman
- Herbert Jaeger
- John Hale
- Brian Roark
- Roger Levy
- David F. Harwath
- Diederik P. Kingma
- Geoffrey E. Hinton
- Kaiming He
- Harald Haas
Who trusts Stefan L. Frank overall
- Peter Alexander Jansen
- Felix Hill
- Scott Watter
- Andy Clark
- John Wieting
- Andrew Kyle Lampinen
- David F. Harwath
- Richard Futrell
- Gina R. Kuperberg
- R. Schneider
Papers from Stefan L. Frank's perspective
Ordered by how strongly the researchers Stefan L. Frank trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Language learning using Speech to Image retrieval (2019) — 46 citations
- Learn more by training less: systematicity in sentence processing by recurrent networks (2006) — 36 citations
- Insensitivity of the Human Sentence-Processing System to Hierarchical Structure (2011) — 226 citations