5 papers
A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models
Abhiram Kandiyana, Ankur Mali, Lawrence O. Hall +2
Deep-learning pipelines for microscopy image classification often require expensive, labor- and time-intensive expert annotation to produce high-quality ground truth for training.…
Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasets
Duong Mai, Lawrence Hall
Deep learned (DL) models for image recognition have been shown to fail to generalize to data from different devices, populations, etc. COVID-19 detection from Chest X-rays (CXRs),…
Integral Signatures of Activation Functions: A 9-Dimensional Taxonomy and Stability Theory for Deep Learning
Ankur Mali, Lawrence Hall, Jake Williams +1
Activation functions govern the expressivity and stability of neural networks, yet existing comparisons remain largely heuristic. We propose a rigorous framework for their classifi…
Reducing Data Requirements for Sequence-Property Prediction in Copolymer Compatibilizers via Deep Neural Network Tuning
Md Mushfiqul Islam, Nishat N. Labiba, Lawrence O. Hall +1
Synthetic sequence-controlled polymers promise to transform polymer science by combining the chemical versatility of synthetic polymers with the precise sequence-mediated functiona…
Active Prompt Tuning Enables Gpt-40 To Do Efficient Classification Of Microscopy Images
Abhiram Kandiyana, Peter R. Mouton, Yaroslav Kolinko +2
Traditional deep learning-based methods for classifying cellular features in microscopy images require time- and labor-intensive processes for training models. Among the current li…