Hayden S. Helm

Artificial Intelligence · 2019–2026

A researcher in the Science corpus: 24 papers; trusts 112 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 248 citations into it
• Representation & Self-Supervised Learning — 81 citations into it; #2,298 of 3,145 by trust here, #2,042 by citations
• Computer Vision and Pattern Recognition — 78 citations into it
• Natural Language Processing — 58 citations into it
• Large Language Models — 52 citations into it; #690 of 799 by trust here, #621 by citations
• Statistics and Probability — 45 citations into it; #7,146 of 12,932 by trust here, #2,407 by citations
• Evaluation & Benchmarks — 28 citations into it; #2,427 of 3,147 by trust here, #2,442 by citations
• Statistical Learning Theory — 21 citations into it

Whom Hayden S. Helm trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Ilya Sutskever
7. Jonathan Baxter
8. Kaiming He
9. Alec Radford
10. Brandon Duderstadt

Who trusts Hayden S. Helm overall

1. Carey E. Priebe
2. Aranyak Acharyya
3. Brandon Duderstadt
4. Weiwei Yang
5. Youngser Park
6. Eric W. Bridgeford
7. M. Trosset
8. Ashwin De Silva

Papers from Hayden S. Helm's perspective

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

1. ImageNet classification with deep convolutional neural networks
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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• A partition-based similarity for classification distributions (2020) — 6 citations
• Comparing Foundation Models using Data Kernels (2023) — 16 citations
• A Statistical Turing Test for Generative Models (2023)
• A Model of the Language Process (2026) — 1 citation
• Query-efficient model evaluation using cached responses (2026)
• Control Charts for Multi-agent Systems (2026) — 1 citation
• Toward A Digital Twin of U.S. Congress (2026) — 0 citations
• Jailbreak susceptibility prediction and mitigation via the behavioral geometry of models (2026)
• Black-box model classification under the discriminative factorization (2026)
• Detecting Perspective Shifts in Multi-agent Systems (2025)
• Testing for LLM response differences: the case of a composite null consisting of semantically irrelevant query perturbations (2025)
• Toward a digital twin of U.S. Congress (2025) — 1 citation
• Investigating social alignment via mirroring in a system of interacting language models (2024)
• Consistent estimation of generative model representations in the data kernel perspective space (2024)
• Tracking the perspectives of interacting language models (2024) — 17 citations
• Statistical inference on black-box generative models in the data kernel perspective space (2024) — 12 citations
• Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis (2023)
• Mental State Classification Using Multi-Graph Features (2022)
• Inducing a hierarchy for multi-class classification problems (2021)
• Omnidirectional Transfer for Quasilinear Lifelong Learning (2021)