Ashwin K. Vijayakumar

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 66 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #23,544 of 35,345 by trust here, #6,517 by citations
• Natural Language Processing — 41 citations into it; #5,658 of 5,688 by trust here, #2,333 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #14,616 of 21,575 by trust here, #5,565 by citations
• Multimodal Learning — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Model Architectures — 8 citations into it
• Code Generation & Program Synthesis — 6 citations into it
• Information Systems — 5 citations into it

Whom Ashwin K. Vijayakumar trusts overall

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

1. Alex Graves
2. Oriol Vinyals
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ilya Sutskever
7. Kaiming He
8. Alex Krizhevsky
9. Jiwei Li
10. Sepp Hochreiter

Who trusts Ashwin K. Vijayakumar overall

1. Yixin Liu
2. Ilia Kulikov
3. Hannah Rashkin
4. Matteo Stefanini
5. Unnat Jain
6. Kevin Ellis
7. Kyunghyun Cho
8. Eyal Ben-David
9. Myle Ott
10. Dan Friedman

Papers from Ashwin K. Vijayakumar's perspective

Ordered by how strongly the researchers Ashwin K. Vijayakumar 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models (2016) — 657 citations
• Diverse Beam Search for Improved Description of Complex Scenes (2018) — 289 citations
• Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples (2018) — 178 citations