Ves Stoyanov

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 10 papers; trusts 401 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 493 citations into it
• Natural Language Processing — 307 citations into it; #5,021 of 5,688 by trust here, #3,421 by citations
• Computer Vision and Pattern Recognition — 216 citations into it
• Large Language Models — 167 citations into it
• Evaluation & Benchmarks — 108 citations into it
• Prompting & Reasoning — 85 citations into it
• Representation & Self-Supervised Learning — 67 citations into it; #2,811 of 3,145 by trust here, #1,866 by citations
• Model Architectures — 24 citations into it

Whom Ves Stoyanov trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Jacob Devlin
9. Alex Graves
10. Richard Socher

Who trusts Ves Stoyanov overall

1. Lu Wang
2. Shuyang Cao

Papers from Ves Stoyanov's perspective

Ordered by how strongly the researchers Ves Stoyanov 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. 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. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Efficient Large Scale Language Modeling with Mixtures of Experts (2021) — 248 citations
• Self-training Improves Pre-training for Natural Language Understanding (2020) — 173 citations
• Prompt-free and Efficient Few-shot Learning with Language Models (2022) — 76 citations
• Few-shot Learning with Multilingual Generative Language Models (2021) — 384 citations
• Training Trajectories of Language Models Across Scales (2022) — 81 citations
• LEVER: Learning to Verify Language-to-Code Generation with Execution (2023) — 351 citations
• Preserving integrity in online social networks (2020) — 64 citations
• Improving In-Context Few-Shot Learning via Self-Supervised Training (2022) — 82 citations
• Few-shot Learning with Multilingual Language Models (2021) — 281 citations
• Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning (2020) — 614 citations