Harsh Jhamtani

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 4 papers; trusts 90 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 147 citations into it; #13,029 of 35,345 by trust here, #7,062 by citations
• Natural Language Processing — 146 citations into it; #2,744 of 5,688 by trust here, #1,874 by citations
• Evaluation & Benchmarks — 77 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it; #9,678 of 21,575 by trust here, #15,955 by citations
• Information Systems — 15 citations into it
• Large Language Models — 10 citations into it
• Model Architectures — 5 citations into it

Whom Harsh Jhamtani trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Oriol Vinyals
5. Ilya Sutskever
6. Franz Joseph Och
7. Jimmy Ba
8. Alex Krizhevsky
9. Christopher D. Manning
10. Jacob Devlin

Who trusts Harsh Jhamtani overall

1. Sarah Wiegreffe
2. Antonia Creswell
3. Ana Marasovic
4. Sudha Rao
5. Murray Shanahan
6. Zhiqiang Hu
7. Parker Riley
8. Di Jin
9. Alexey Romanov
10. Swarnadeep Saha

Papers from Harsh Jhamtani's perspective

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

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

Their papers

• The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021) — 329 citations
• Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering (2020) — 81 citations
• Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models (2017) — 190 citations
• Identifying Purchase Intent from Social Posts (2014) — 53 citations