Stephen Marcus McAleer

Artificial Intelligence · with Baldi · 2019–2023

A researcher in the Science corpus: 4 papers; trusts 66 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #27,384 of 35,345 by trust here, #11,750 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #16,827 of 21,575 by trust here, #10,334 by citations
• Natural Language Processing — 26 citations into it
• Large Language Models — 23 citations into it; #541 of 799 by trust here, #564 by citations
• Prompting & Reasoning — 20 citations into it
• Reinforcement Learning — 17 citations into it
• LLM Agents & Tool Use — 14 citations into it
• Information Systems — 9 citations into it

Whom Stephen Marcus McAleer trusts overall

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

1. Kaiming He
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Dimitri P. Bertsekas
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. Xavier Glorot
10. J. Urgen Schmidhuber

Who trusts Stephen Marcus McAleer overall

1. Zhibin Zhao
2. Xinyun Chen
3. Long Wen
4. Jinyang Jiao
5. Huan Wang
6. Min Xia
7. Rui Yang

Papers from Stephen Marcus McAleer's perspective

Ordered by how strongly the researchers Stephen Marcus McAleer 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. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Language Models are Few-Shot Learners (Neural Information Processing Systems)

Their papers

• Language Models can Solve Computer Tasks (2023) — 522 citations
• Solving the Rubik’s cube with deep reinforcement learning and search (2019) — 210 citations
• AI Alignment: A Comprehensive Survey (2023) — 376 citations
• Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning (2019) — 1,371 citations