6 papers
SAGE: Saliency-Guided Contrastive Embeddings
Colton R. Crum, Christopher Sweet, Adam Czajka
Integrating human perceptual priors into the training of neural networks has been shown to raise model generalization, serve as an effective regularizer, and align models with huma…
Almost Right: Making First-Layer Kernels Nearly Orthogonal Improves Model Generalization
Colton R. Crum, Adam Czajka
Despite several algorithmic advances in the training of convolutional neural networks (CNNs) over the years, their generalization capabilities are still subpar across several perti…
MENTOR: Human Perception-Guided Pretraining for Increased Generalization
Colton R. Crum, Adam Czajka
Leveraging human perception into training of convolutional neural networks (CNN) has boosted generalization capabilities of such models in open-set recognition tasks. One of the ac…
Regulating Multifunctionality
Cary Coglianese, Colton R. Crum
Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. By their very nature, foundation models…
Taking Training Seriously: Human Guidance and Management-Based Regulation of Artificial Intelligence
Cary Coglianese, Colton R. Crum
Fervent calls for more robust governance of the harms associated with artificial intelligence (AI) are leading to the adoption around the world of what regulatory scholars have cal…
Grains of Saliency: Optimizing Saliency-based Training of Biometric Attack Detection Models
Colton R. Crum, Samuel Webster, Adam Czajka
Incorporating human-perceptual intelligence into model training has shown to increase the generalization capability of models in several difficult biometric tasks, such as presenta…