Giulia DeSalvo

Artificial Intelligence · 2016

A researcher in the Science corpus: 4 papers; trusts 42 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #8,618 of 35,345 by trust here, #6,103 by citations
• Optimization & Training — 30 citations into it; #1,209 of 2,601 by trust here, #666 by citations
• Management Science and Operations Research — 28 citations into it; #3,384 of 11,946 by trust here, #819 by citations
• Computational Theory and Mathematics — 13 citations into it; #3,671 of 12,182 by trust here, #791 by citations
• Computer Networks and Communications — 12 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Neural Architecture Search & AutoML — 10 citations into it; #101 of 283 by trust here, #91 by citations

Whom Giulia DeSalvo trusts overall

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

1. Alex Krizhevsky
2. Frank Hutter
3. James Bergstra
4. Jasper Snoek
5. Yoshua Bengio
6. Kevin Swersky
7. Kevin G. Jamieson
8. Geoffrey E. Hinton
9. Katharina Eggensperger
10. Hugo Larochelle

Who trusts Giulia DeSalvo overall

1. Aaron Klein
2. Frank Hutter
3. Xuanyi Dong
4. Stefan Falkner
5. David Madras
6. Liam Li
7. Marc-André Zöller
8. Hussein Mozannar
9. Matthias Feurer
10. Nicoló Fusi

Papers from Giulia DeSalvo's perspective

Ordered by how strongly the researchers Giulia DeSalvo 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. Learning Multiple Layers of Features from Tiny Images
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (2016) — 2,847 citations
• Learning with Rejection (2016) — 341 citations
• Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization (2016) — 197 citations
• Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits (2016) — 148 citations