Vinit Ravishankar

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #25,930 of 35,345 by trust here, #23,077 by citations
• Natural Language Processing — 49 citations into it; #4,270 of 5,688 by trust here, #2,567 by citations
• Computer Vision and Pattern Recognition — 16 citations into it; #15,938 of 21,575 by trust here, #15,529 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Model Architectures — 5 citations into it

Whom Vinit Ravishankar trusts overall

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

1. Alexis Conneau
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Mikel Artetxe
7. Ashish Vaswani
8. D. Kondratyuk
9. Tomas Mikolov
10. Christopher D. Manning

Who trusts Vinit Ravishankar overall

1. Goran Glavas
2. Jonas Pfeiffer
3. Ivan Vulic
4. Phillip Rust

Papers from Vinit Ravishankar's perspective

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

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

Their papers

• From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers (2020) — 363 citations
• Are Multilingual Neural Machine Translation Models Better at Capturing Linguistic Features? (2020) — 10 citations
• Universal Dependencies 2.1 (2017) — 425 citations