3 citations · 5 across the 5 of their papers we have counts for
5 papers
What do Geometric Hallucination Detection Metrics Actually Measure?
Eric Yeats, John Buckheit, Sarah Scullen +9
Hallucination remains a barrier to deploying generative models in high-consequence applications. This is especially true in cases where external ground truth is not readily availab…
GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding
Stefan Dernbach, Khushbu Agarwal, Alejandro Zuniga +2
Integrating large language models (LLMs) with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As…
Evaluating generative networks using Gaussian mixtures of image features
Lorenzo Luzi, Carlos Ortiz Marrero, Nile Wynar +2
We develop a measure for evaluating the performance of generative networks given two sets of images. A popular performance measure currently used to do this is the Fréchet Inceptio…
The Outer Product Structure of Neural Network Derivatives
Craig Bakker, Michael J. Henry, Nathan O. Hodas
In this paper, we show that feedforward and recurrent neural networks exhibit an outer product derivative structure but that convolutional neural networks do not. This structure ma…
Multi-modal Geolocation Estimation Using Deep Neural Networks
Jesse M. Johns, Jeremiah Rounds, Michael J. Henry
Estimating the location where an image was taken based solely on the contents of the image is a challenging task, even for humans, as properly labeling an image in such a fashion r…