Alessandro Stolfo

Artificial Intelligence · 2022

A researcher in the Science corpus: 2 papers; trusts 90 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #28,174 of 35,345 by trust here, #30,347 by citations
• Natural Language Processing — 29 citations into it
• Large Language Models — 28 citations into it; #565 of 799 by trust here, #757 by citations
• Prompting & Reasoning — 19 citations into it; #234 of 359 by trust here, #324 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Information Systems — 6 citations into it

Whom Alessandro Stolfo trusts overall

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

1. Geoffrey E. Hinton
2. Matt Post
3. Yoshua Bengio
4. Ilya Sutskever
5. Diederik P. Kingma
6. Kyunghyun Cho
7. Oriol Vinyals
8. Alec Radford
9. Alex Krizhevsky
10. Ashish Vaswani

Who trusts Alessandro Stolfo overall

1. Zirui Zhao
2. Jiaxin Huang

Papers from Alessandro Stolfo's perspective

Ordered by how strongly the researchers Alessandro Stolfo 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Distilling Multi-Step Reasoning Capabilities of Large Language Models into Smaller Models via Semantic Decompositions (2022) — 49 citations
• Distilling Reasoning Capabilities into Smaller Language Models (2022) — 270 citations