Nishant Subramani

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 9 papers; trusts 105 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 452 citations into it; #27,644 of 35,345 by trust here, #4,772 by citations
• Natural Language Processing — 356 citations into it
• Evaluation & Benchmarks — 189 citations into it
• Computer Vision and Pattern Recognition — 117 citations into it; #17,650 of 21,575 by trust here, #18,782 by citations
• Representation & Self-Supervised Learning — 25 citations into it
• Model Architectures — 24 citations into it
• Optimization & Training — 15 citations into it
• Generative Models — 14 citations into it

Whom Nishant Subramani trusts overall

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

1. Alex Graves
2. Kyunghyun Cho
3. Yoshua Bengio
4. Ilya Sutskever
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Tomas Mikolov
8. Sepp Hochreiter
9. Alex Krizhevsky
10. Dzmitry Bahdanau

Who trusts Nishant Subramani overall

1. Xiang Lisa Li
2. Andrea Madotto
3. Percy Liang
4. Etsuko Ishii
5. Sumanth Dathathri
6. Fan-Keng Sun
7. Kevin Yang

Papers from Nishant Subramani's perspective

Ordered by how strongly the researchers Nishant Subramani 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021) — 329 citations
• Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets (2021) — 356 citations
• Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022) — 65 citations
• Can Unconditional Language Models Recover Arbitrary Sentences? (2019) — 29 citations
• GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
• OLMo: Accelerating the Science of Language Models (2024) — 700 citations
• Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2024) — 519 citations
• Don’t Say What You Don’t Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search (2022) — 36 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations