Carina Silberer
Artificial Intelligence · 2012–2014
A researcher in the Science corpus: 2 papers; trusts 103 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 21 citations into it; #9,089 of 35,345 by trust here, #20,377 by citations
- Natural Language Processing — 20 citations into it; #2,198 of 5,688 by trust here, #4,032 by citations
- Computer Vision and Pattern Recognition — 14 citations into it; #6,414 of 21,575 by trust here, #13,542 by citations
- Representation & Self-Supervised Learning — 14 citations into it; #739 of 3,145 by trust here, #1,754 by citations
- Developmental and Educational Psychology — 10 citations into it
- Cognitive Neuroscience — 7 citations into it
- Multimodal Learning — 7 citations into it
- Information Retrieval & Search — 6 citations into it
Whom Carina Silberer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Richard Socher
- David G. Lowe
- Andrew Y. Ng
- Geoffrey E. Hinton
- Dekang Lin
- David M. Blei
- T. Landauer
- Tomas Mikolov
- Ken McRae
Who trusts Carina Silberer overall
- Douwe Kiela
- Mark Yatskar
- Anna Korhonen
- Angeliki Lazaridou
- Tadas Baltrusaitis
- Shaonan Wang
- Felix Hill
- Marco Baroni
- Junhua Mao
- Chao Zhang
Papers from Carina Silberer's perspective
Ordered by how strongly the researchers Carina Silberer trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learning Grounded Meaning Representations with Autoencoders (2014) — 229 citations
- Grounded Models of Semantic Representation (2012) — 100 citations