3 papers
cs.LG2026
Learning Not To Interfere: Adapting Experts For Model Merging
Pratik Ramesh, George Stoica, Arun Iyer +2
Model merging has shown that multitask models can be created by directly combining the parameters of different models that are each specialized on tasks of interest. However, model…
cs.CV2024
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…
cs.CV2023
FACTS: First Amplify Correlations and Then Slice to Discover Bias
Sriram Yenamandra, Pratik Ramesh, Viraj Prabhu +1
Computer vision datasets frequently contain spurious correlations between task-relevant labels and (easy to learn) latent task-irrelevant attributes (e.g. context). Models trained…