Carey E. Priebe

Artificial Intelligence · arXiv.org · 2021–2026

A researcher in the Science corpus: 24 papers; trusts 77 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 207 citations into it
• Computer Vision and Pattern Recognition — 63 citations into it
• Large Language Models — 54 citations into it; #663 of 799 by trust here, #621 by citations
• Natural Language Processing — 53 citations into it
• Representation & Self-Supervised Learning — 43 citations into it; #2,265 of 3,145 by trust here, #1,943 by citations
• Statistics and Probability — 32 citations into it; #7,133 of 12,932 by trust here, #2,579 by citations
• Evaluation & Benchmarks — 23 citations into it; #2,378 of 3,147 by trust here, #2,442 by citations
• Model Architectures — 13 citations into it

Whom Carey E. Priebe trusts overall

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

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

Who trusts Carey E. Priebe overall

1. Hayden S. Helm
2. Aranyak Acharyya
3. Brandon Duderstadt

Papers from Carey E. Priebe's perspective

Ordered by how strongly the researchers Carey E. Priebe 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. Attention is All you Need (Neural Information Processing Systems)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Comparing Foundation Models using Data Kernels (2023) — 16 citations
• A Statistical Turing Test for Generative Models (2023)
• Query-efficient model evaluation using cached responses (2026)
• Control Charts for Multi-agent Systems (2026) — 1 citation
• Recovering manifold structure in LLM responses through a joint Euclidean mirror (2026)
• Toward A Digital Twin of U.S. Congress (2026) — 0 citations
• Data Kernel Perspective Space Performance Guarantees for Synthetic Data from Transformer Models (2026)
• Black-box model classification under the discriminative factorization (2026)
• Testing for LLM response differences: the case of a composite null consisting of semantically irrelevant query perturbations (2025)
• Concentration bounds on response-based vector embeddings of black-box generative models (2025)
• Convergence Guarantees for Response Prediction for Latent Structure Network Time Series (2025)
• Toward a digital twin of U.S. Congress (2025) — 1 citation
• Gaussian mixture models as a proxy for interacting language models (2025)
• LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs (2025)
• 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)
• Discovering a change point in a time series of organoid networks via the iso-mirror (2023)