Aditya Siddhant

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 11 papers; trusts 187 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 327 citations into it; #6,387 of 35,345 by trust here, #2,084 by citations
• Natural Language Processing — 291 citations into it; #2,960 of 5,688 by trust here, #912 by citations
• Computer Vision and Pattern Recognition — 118 citations into it; #8,527 of 21,575 by trust here, #4,105 by citations
• Evaluation & Benchmarks — 85 citations into it; #1,482 of 3,147 by trust here, #899 by citations
• Large Language Models — 59 citations into it
• Representation & Self-Supervised Learning — 45 citations into it
• Model Architectures — 23 citations into it
• General Social Sciences — 9 citations into it

Whom Aditya Siddhant trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Noam Shazeer
5. Ilya Sutskever
6. Guillaume Lample
7. Alexis Conneau
8. Ashish Vaswani
9. Mikel Artetxe
10. Alex Krizhevsky

Who trusts Aditya Siddhant overall

1. Junjie Hu
2. Zewen Chi
3. Ankur Bapna
4. Mihir Kale
5. Noah Constant
6. Sebastian Ruder
7. Orhan Firat
8. Melvin Johnson
9. Graham Neubig
10. Surangika Ranathunga

Papers from Aditya Siddhant's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Gradient-based learning applied to document recognition

Their papers

• mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2020) — 3,278 citations
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization (2020) — 1,171 citations
• Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation (2019) — 76 citations
• Building Machine Translation Systems for the Next Thousand Languages (2022) — 120 citations
• XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation (2021) — 234 citations
• Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation (2020) — 88 citations
• Reinforced Self-Training (ReST) for Language Modeling (2023) — 473 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Explicit Alignment Objectives for Multilingual Bidirectional Encoders (2020) — 65 citations
• nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? (2021) — 28 citations
• Dialect-robust Evaluation of Generated Text (2022) — 28 citations