9 citations · 13 across the 8 of their papers we have counts for
9 papers
RAGRank: Using PageRank to Counter Poisoning in CTI LLM Pipelines
Austin Jia, Avaneesh Ramesh, Zain Shamsi +2
Retrieval-Augmented Generation (RAG) has emerged as the dominant architectural pattern to operationalize Large Language Model (LLM) usage in Cyber Threat Intelligence (CTI) systems…
Semantic Convergence: Investigating Shared Representations Across Scaled LLMs
Daniel Son, Sanjana Rathore, Andrew Rufail +6
We investigate feature universality in Gemma-2 language models (Gemma-2-2B and Gemma-2-9B), asking whether models with a four-fold difference in scale still converge on comparable…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation
Ali Abbasi, Chayne Thrash, Elaheh Akbari +2
The rapid progress of AI, combined with its unprecedented public adoption and the propensity of large neural networks to memorize training data, has given rise to significant data…
Development of a Neural Network-based Method for Improved Imputation of Missing Values in Time Series Data by Repurposing DataWig
Daniel Zhang
Time series data are observations collected over time intervals. Successful analysis of time series data captures patterns such as trends, cyclicity and irregularity, which are cru…
The AI Index 2022 Annual Report
Daniel Zhang, Nestor Maslej, Erik Brynjolfsson +10
Welcome to the fifth edition of the AI Index Report! The latest edition includes data from a broad set of academic, private, and nonprofit organizations as well as more self-collec…
The AI Index 2021 Annual Report
Daniel Zhang, Saurabh Mishra, Erik Brynjolfsson +10
Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organiza…
Online Boosting for Multilabel Ranking with Top-k Feedback
Vinod Raman, Daniel T. Zhang, Young Hun Jung +1
We present online boosting algorithms for multilabel ranking with top-k feedback, where the learner only receives information about the top k items from the ranking it provides. We…
Online Multiclass Boosting with Bandit Feedback
Daniel T. Zhang, Young Hun Jung, Ambuj Tewari
We present online boosting algorithms for multiclass classification with bandit feedback, where the learner only receives feedback about the correctness of its prediction. We propo…