William La Cava

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 3 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Genetics — 8 citations into it
• Optimization & Training — 8 citations into it
• Information Systems — 7 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Statistics and Probability — 6 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom William La Cava trusts overall

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

1. Ronald J. Williams
2. Michael D. Schmidt
3. Jaka Demsar
4. Cynthia Rudin
5. Tianqi Chen
6. Trevor J. Hastie
7. Igor Kononenko
8. S. Udrescu
9. Brenden K. Petersen
10. David Ruppert

Who trusts William La Cava overall

1. Nour Makke
2. Alexey Ignatiev
3. Mikoláš Janota
4. André Schidler
5. Nina Narodytska
6. Sanjay Chawla
7. Harvey Wu
8. Chi Wang

Papers from William La Cava's perspective

Ordered by how strongly the researchers William La Cava 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Regression Shrinkage and Selection via the Lasso
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Their papers

• PMLB: a large benchmark suite for machine learning evaluation and comparison (2017) — 459 citations
• Relief-Based Feature Selection: Introduction and Review (2017) — 1,180 citations
• Contemporary Symbolic Regression Methods and their Relative Performance (2021) — 418 citations