Shruti Bhosale

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 11 papers; trusts 92 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 476 citations into it; #13,296 of 35,345 by trust here, #1,340 by citations
• Natural Language Processing — 276 citations into it
• Computer Vision and Pattern Recognition — 182 citations into it
• Large Language Models — 131 citations into it
• Evaluation & Benchmarks — 114 citations into it
• Efficiency & Compression — 59 citations into it; #599 of 1,264 by trust here, #490 by citations
• Model Architectures — 48 citations into it
• Optimization & Training — 22 citations into it

Whom Shruti Bhosale trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Jimmy Ba
5. Ilya Sutskever
6. Alex Krizhevsky
7. Kaiming He
8. Noam Shazeer
9. Yann LeCun
10. Alex Graves

Who trusts Shruti Bhosale overall

1. Barret Zoph
2. William Fedus
3. Kurt Shuster
4. Michael Lewis
5. Canwen Xu
6. Joan Puigcerver
7. Weilin Cai
8. Stephen Roller
9. Tim Dettmers
10. Joseph Dean

Papers from Shruti Bhosale's perspective

Ordered by how strongly the researchers Shruti Bhosale 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — 17,670 citations
• BASE Layers: Simplifying Training of Large, Sparse Models (2021) — 411 citations
• Efficient Large Scale Language Modeling with Mixtures of Experts (2021) — 248 citations
• Beyond English-Centric Multilingual Machine Translation (2020) — 1,098 citations
• Tricks for Training Sparse Translation Models (2021) — 23 citations
• Few-shot Learning with Multilingual Generative Language Models (2021) — 384 citations
• No Language Left Behind: Scaling Human-Centered Machine Translation (2022) — 1,884 citations
• Effective Long-Context Scaling of Foundation Models (2023) — 346 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Few-shot Learning with Multilingual Language Models (2021) — 281 citations