Pranava Swaroop Madhyastha

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 201 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it
• Natural Language Processing — 52 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Multimodal Learning — 28 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Model Architectures — 12 citations into it
• Machine Translation — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Pranava Swaroop Madhyastha trusts overall

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

1. Kaiming He
2. Desmond Elliott
3. Matthew D. Zeiler
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Ilya Sutskever
9. Yann LeCun
10. Karen Simonyan

Who trusts Pranava Swaroop Madhyastha overall

1. Tejas Srinivasan
2. Xin Eric Wang
3. Ananya B. Sai
4. Zhiyong Wu

Papers from Pranava Swaroop Madhyastha's perspective

Ordered by how strongly the researchers Pranava Swaroop Madhyastha 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Probing the Need for Visual Context in Multimodal Machine Translation (2019) — 158 citations
• Sheffield MultiMT: Using Object Posterior Predictions for Multimodal Machine Translation (2017) — 17 citations
• WMDO: Fluency-based Word Mover’s Distance for Machine Translation Evaluation (2019) — 27 citations
• Learning from Multiview Correlations in Open-domain Videos (2018) — 11 citations
• The TALP–UPC Spanish–English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System (2016) — 13 citations