Sergio Ramírez-Gallego

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 12 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #31,601 of 35,345 by trust here, #22,463 by citations
• Information Systems — 61 citations into it
• Computer Vision and Pattern Recognition — 40 citations into it
• Computer Networks and Communications — 32 citations into it
• ML Systems & Hardware — 18 citations into it
• Statistical Learning Theory — 12 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Sergio Ramírez-Gallego trusts overall

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

1. Muthu Dayalan
2. Tom White
3. Thomas M. Cover
4. João Gama
5. Salvador Garcia
6. Dong C. Liu
7. Pierre Baldi
8. Robert E. Schapire
9. Balaji Lakshminarayanan
10. Miroslav Kubat

Who trusts Sergio Ramírez-Gallego overall

1. Alberto Fernández
2. Francisco Herrera
3. Salvador Garcia

Papers from Sergio Ramírez-Gallego's perspective

Ordered by how strongly the researchers Sergio Ramírez-Gallego trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce (2018) — 132 citations
• Big Data Preprocessing: Enabling Smart Data (2020) — 70 citations
• A survey on data preprocessing for data stream mining: Current status and future directions (2017) — 457 citations
• kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data (2017) — 309 citations
• Big data preprocessing: methods and prospects (2016) — 594 citations