works on

From the 1 of 6 linked papers with an AI index.

activity
20242026
collaborators

6 papers

cs.LG2026

RAFP: Identifying LLM Lineages via Rare-Region Fingerprints

Yun-Yun Tsai, Jia Hao Liang, Chuan Guo +2

The paper proposes RAFP, a non‑invasive method that creates fingerprints from rare prompt‑response regions to reliably identify the lineage of large language models even after vari…

cs.CV2025

GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition

Vikram V. Ramaswamy, Sing Yu Lin, Dora Zhao +4

Current dataset collection methods typically scrape large amounts of data from the web. While this technique is extremely scalable, data collected in this way tends to reinforce st…

cs.LG2025

NOMAD Projection

Brandon Duderstadt, Zach Nussbaum, Laurens van der Maaten

The rapid adoption of generative AI has driven an explosion in the size of datasets consumed and produced by AI models. Traditional methods for unstructured data visualization, suc…

cs.AI2024

The Llama 3 Herd of Models

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…

cs.AI2024

Law of the Weakest Link: Cross Capabilities of Large Language Models

Ming Zhong, Aston Zhang, Xuewei Wang +14

The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities acros…

cs.LG2024

Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds

Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten +2

Protecting privacy during inference with deep neural networks is possible by adding noise to the activations in the last layers prior to the final classifiers or other task-specifi…