Akash Kumar Mohankumar

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 3 papers; trusts 384 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 170 citations into it; #21,431 of 35,345 by trust here, #16,471 by citations
• Natural Language Processing — 169 citations into it; #5,557 of 5,688 by trust here, #4,032 by citations
• Evaluation & Benchmarks — 111 citations into it
• Computer Vision and Pattern Recognition — 47 citations into it; #21,124 of 21,575 by trust here, #13,815 by citations
• Representation & Self-Supervised Learning — 17 citations into it
• Information Systems — 11 citations into it
• Reinforcement Learning — 10 citations into it
• Multimodal Learning — 9 citations into it

Whom Akash Kumar Mohankumar trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Chin-Yew Lin
4. Ilya Sutskever
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ondrej Bojar
8. Yann LeCun
9. Alex Graves
10. Maja Popovic

Who trusts Akash Kumar Mohankumar overall

1. Ananya B. Sai
2. Prakhar Gupta
3. Huihan Li
4. Tanay Dixit
5. Zheng Ye

Papers from Akash Kumar Mohankumar's perspective

Ordered by how strongly the researchers Akash Kumar Mohankumar 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• A Survey of Evaluation Metrics Used for NLG Systems (2020) — 344 citations
• Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining (2020) — 83 citations
• Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons (2022) — 10 citations