4 papers
Decompose, Mix, Adapt: A Unified Framework for Parameter-Efficient Neural Network Recombination and Compression
Nazia Tasnim, Shrimai Prabhumoye, Bryan A. Plummer
Parameter Recombination (PR) methods aim to efficiently compose the weights of a neural network for applications like Parameter-Efficient FineTuning (PEFT) and Model Compression (M…
CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images
Vidit Agrawal, John Peters, Tyler N. Thompson +13
Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, models used to quantify cellul…
Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization
Siqi Wang, Aoming Liu, Bryan A. Plummer
Methods addressing Learning with Noisy Labels (LNL) and multi-source Domain Generalization (DG) use training techniques to improve downstream task performance in the presence of la…
ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning
Chau Pham, Juan C. Caicedo, Bryan A. Plummer
Prior work using Masked Autoencoders (MAEs) typically relies on random patch masking based on the assumption that images have significant redundancies across different channels, al…