Jaime Zabalza
Cognitive Neuroscience · 2015–2019
A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 24 citations into it
- Cellular and Molecular Neuroscience — 12 citations into it
- Electrical and Electronic Engineering — 12 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
Whom Jaime Zabalza trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Gert Pfurtscheller
- Dervis Karaboga
- Yann LeCun
- Yushi Chen
- Gernot R. Müller-Putz
- Yoshua Bengio
- Luis F. Nicolás-Alonso
- Yousef Rezaei Tabar
- Bernhard Graimann
- A. K. Qin
Who trusts Jaime Zabalza overall
Papers from Jaime Zabalza's perspective
Ordered by how strongly the researchers Jaime Zabalza 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019) — 508 citations
- Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging (2016) — 363 citations
- Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions (2015) — 58 citations