Ronak D. Mehta

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Statistics and Probability — 15 citations into it
• Statistical Learning Theory — 13 citations into it
• Natural Language Processing — 13 citations into it
• Model Architectures — 9 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Ronak D. Mehta trusts overall

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

1. Jonathan Baxter
2. Yoshua Bengio
3. Alex Krizhevsky
4. Vladimir Naumovich Vapnik
5. C. Stone
6. Kyle Duffie
7. Kurt Hornik
8. Jörg Tiedemann
9. Katherine L. Silversides
10. Yann LeCun

Who trusts Ronak D. Mehta overall

1. Hayden S. Helm

Papers from Ronak D. Mehta's perspective

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

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

Their papers

• A partition-based similarity for classification distributions (2020) — 6 citations
• Omnidirectional Transfer for Quasilinear Lifelong Learning (2021)
• Towards a theory of out-of-distribution learning (2021)
• A general approach to progressive learning (2020) — 10 citations