María José Castro Bleda

Artificial Intelligence · 2009–2014

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

Subfields

• Artificial Intelligence — 28 citations into it; #27,309 of 35,345 by trust here, #30,347 by citations
• Natural Language Processing — 20 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it; #16,119 of 21,575 by trust here, #17,757 by citations
• Large Language Models — 10 citations into it
• Model Architectures — 9 citations into it
• Speech & Audio — 8 citations into it
• Media Technology — 6 citations into it; #3,106 of 3,621 by trust here, #2,954 by citations

Whom María José Castro Bleda trusts overall

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

1. Andreas Stolcke
2. Holger Schwenk
3. Yoshua Bengio
4. Urs-Viktor Marti
5. Alex Graves
6. Frederick Jelinek
7. David E. Rumelhart
8. Geoffrey E. Hinton
9. Horst Bunke
10. Alessandro Vinciarelli

Who trusts María José Castro Bleda overall

1. J. Urgen Schmidhuber
2. Li Deng
3. Simon Wiesler
4. Francisco Zamora-Martínez
5. Volkmar Frinken
6. Thomas Breuel

Papers from María José Castro Bleda's perspective

Ordered by how strongly the researchers María José Castro Bleda trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Fast Evaluation of Connectionist Language Models (2009) — 19 citations
• Neural network language models for off-line handwriting recognition (2014) — 79 citations
• Long-short term memory neural networks language modeling for handwriting recognition (2012) — 28 citations
• Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models (2011) — 342 citations