74 citations · 101 across the 15 of their papers we have counts for
5 papers · 1 filter
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images
Nam Cao, Olga Saukh
Distribution shifts are characterized by differences between the training and test data distributions. They can significantly reduce the accuracy of machine learning models deploye…
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints
Francesco Corti, Balz Maag, Joachim Schauer +2
Deep learning models deployed on edge devices frequently encounter resource variability, which arises from fluctuating energy levels, timing constraints, or prioritization of other…
Subspace-Configurable Networks
Dong Wang, Olga Saukh, Xiaoxi He +1
While the deployment of deep learning models on edge devices is increasing, these models often lack robustness when faced with dynamic changes in sensed data. This can be attribute…
DataComp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang +31
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model arch…
The Role of Pre-training Data in Transfer Learning
Rahim Entezari, Mitchell Wortsman, Olga Saukh +3
The transfer learning paradigm of model pre-training and subsequent fine-tuning produces high-accuracy models. While most studies recommend scaling the pre-training size to benefit…