6 citations · 17 across the 5 of their papers we have counts for
4 papers · 1 filter
Contrastive Flow Matching
George Stoica, Vivek Ramanujan, Xiang Fan +3
Unconditional flow-matching trains diffusion models to transport samples from a source distribution to a target distribution by enforcing that the flows between sample pairs are un…
Model merging with SVD to tie the Knots
George Stoica, Pratik Ramesh, Boglarka Ecsedi +2
Recent model merging methods demonstrate that the parameters of fully-finetuned models specializing in distinct tasks can be combined into one model capable of solving all tasks wi…
Analyzing domain shift when using additional data for the MICCAI KiTS23 Challenge
George Stoica, Mihaela Breaban, Vlad Barbu
Using additional training data is known to improve the results, especially for medical image 3D segmentation where there is a lack of training material and the model needs to gener…
ZipIt! Merging Models from Different Tasks without Training
George Stoica, Daniel Bolya, Jakob Bjorner +3
Typical deep visual recognition models are capable of performing the one task they were trained on. In this paper, we tackle the extremely difficult problem of combining distinct m…