Yaron Shaposhnik

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #20,910 of 35,345 by trust here, #22,463 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Safety, Robustness & Interpretability — 16 citations into it; #2,022 of 2,251 by trust here, #1,907 by citations
• Computational Theory and Mathematics — 7 citations into it
• Signal Processing — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Yaron Shaposhnik trusts overall

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

1. L. V. D. van der Maaten
2. Marco Tulio Ribeiro
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Scott M. Lundberg
6. David P. Williamson
7. Sameer Singh
8. Cynthia Rudin
9. Uriel Feige
10. Berk Ustun

Who trusts Yaron Shaposhnik overall

1. Cynthia Rudin

Papers from Yaron Shaposhnik's perspective

Ordered by how strongly the researchers Yaron Shaposhnik trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• An Interpretable Model with Globally Consistent Explanations for Credit Risk (2018) — 104 citations
• Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization (2020) — 528 citations
• Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation (2019) — 42 citations