Antonio A. Ginart

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #19,609 of 35,345 by trust here, #13,163 by citations
• Natural Language Processing — 12 citations into it
• Privacy & Security — 11 citations into it; #423 of 709 by trust here, #391 by citations
• Statistics and Probability — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Statistical Learning Theory — 9 citations into it
• Large Language Models — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Antonio A. Ginart trusts overall

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

1. L. Eon Bottou
2. Cynthia Dwork
3. Vito M. R. Muggeo
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Ilya Mironov
7. Alex Krizhevsky
8. Yann LeCun
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Antonio A. Ginart overall

1. Aditya Golatkar
2. Seth Neel
3. Alessandro Achille
4. Meghdad Kurmanji
5. Thomas Baumhauer
6. Quoc Phong Nguyen
7. Laura Graves
8. Ayush Kumar Tarun
9. Alexander Warnecke
10. Ayush Sekhari

Papers from Antonio A. Ginart's perspective

Ordered by how strongly the researchers Antonio A. Ginart 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Making AI Forget You: Data Deletion in Machine Learning (2019) — 709 citations
• Submix: Practical Private Prediction for Large-Scale Language Models (2022) — 33 citations
• Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems (2019) — 114 citations