Yolanda Gil
Computer Networks and Communications · 1989–2020
A researcher in the Science corpus: 15 papers; trusts 17 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 49 citations into it; #6,223 of 9,886 by trust here, #1,370 by citations
- Artificial Intelligence — 34 citations into it; #29,205 of 35,345 by trust here, #13,163 by citations
- Information Systems — 28 citations into it
- Information Systems and Management — 22 citations into it; #2,164 of 3,842 by trust here, #393 by citations
- Sociology and Political Science — 22 citations into it; #22,751 of 22,756 by trust here, #17,766 by citations
- Hardware and Architecture — 15 citations into it
- Optimization & Training — 5 citations into it
Whom Yolanda Gil trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Matthias Feurer
- Frank Hutter
- Nicoló Fusi
- Randal S. Olson
- Ian T Foster
- Roslan Ismail
- Holger H. Hoos
- Cai-Nicolas Ziegler
- Chris J. Thornton
- R. Caruana
Who trusts Yolanda Gil overall
- Marc-André Zöller
- Marco F. Huber
- Jia Yu
- Shaowen Wang
- Tien-Dung Nguyen
- Dakuo Wang
- Russell A. Poldrack
- Lars Kotthoff
- Aram Galstyan
- Rajkumar Buyya
Papers from Yolanda Gil's perspective
Ordered by how strongly the researchers Yolanda Gil trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Learning Multiple Layers of Features from Tiny Images
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Scikit-learn: Machine Learning in Python
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Practical Bayesian Optimization of Machine Learning Algorithms
- Going deeper with convolutions
Their papers
- Amplify scientific discovery with artificial intelligence (2014) — 153 citations
- A survey of trust in computer science and the Semantic Web (2007) — 799 citations
- The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience (2011) — 403 citations
- Mapping Abstract Complex Workflows onto Grid Environments (2003) — 561 citations
- The genetic architecture of the human cerebral cortex (2020) — 777 citations
- Towards human-guided machine learning (2019) — 92 citations
- P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning (2018)
- Enhancing reproducibility for computational methods (2016) — 396 citations
- PROV-DM: The PROV Data Model (2013) — 669 citations
- The Open Provenance Model core specification (v1.1) (2011) — 843 citations
- Examining the Challenges of Scientific Workflows (2007) — 620 citations
- Pegasus: A framework for mapping complex scientific workflows onto distributed systems (2005) — 1,448 citations
- Pegasus: Mapping Scientific Workflows onto the Grid (2004) — 591 citations
- PRODIGY: an integrated architecture for planning and learning (1991)
- Explanation-Based Learning: A Problem Solving Perspective (1989)