1 citations · 1 across the 5 of their papers we have counts for
6 papers
Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering
Austin Meek, Kyle Cox, Matthew Nguyen +2
Reward hacking is a major problem in current production machine learning systems, resulting in costly and dangerous failures. Here we study how to monitor for and mitigate reward h…
Measuring Chain-of-Thought Monitorability Through Faithfulness and Verbosity
Austin Meek, Eitan Sprejer, Iván Arcuschin +2
Chain-of-thought (CoT) outputs let us read a model's step-by-step reasoning. Since any long, serial reasoning process must pass through this textual trace, the quality of the CoT i…
Convolutional Monge Mapping between EEG Datasets to Support Independent Component Labeling
Austin Meek, Carlos H. Mendoza-Cardenas, Austin J. Brockmeier
EEG recordings contain rich information about neural activity but are subject to artifacts, noise, and superficial differences due to sensors, amplifiers, and filtering. Independen…
ABC-SN: Attention Based Classifier for Supernova Spectra
Willow Fox Fortino, Federica B. Bianco, Pavlos Protopapas +2
While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin…
Weakly Supervised Object Segmentation by Background Conditional Divergence
Hassan Baker, Matthew S. Emigh, Austin J. Brockmeier
As a computer vision task, automatic object segmentation remains challenging in specialized image domains without massive labeled data, such as synthetic aperture sonar images, rem…
Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection
Hassan Baker, Austin J. Brockmeier
Detecting brain lesions as abnormalities observed in magnetic resonance imaging (MRI) is essential for diagnosis and treatment. In the search of abnormalities, such as tumors and m…