1 citations · 1 across the 4 of their papers we have counts for
4 papers
Merge to Mix: Mixing Datasets via Model Merging
Zhixu Silvia Tao, Kasper Vinken, Hao-Wei Yeh +2
Mixing datasets for fine-tuning large models (LMs) has become critical for maximizing performance on downstream tasks. However, composing effective dataset mixtures typically relie…
Task Arithmetic Through The Lens Of One-Shot Federated Learning
Zhixu Silvia Tao, Ian Mason, Sanjeev Kulkarni +1
Task Arithmetic is a model merging technique that enables the combination of multiple models' capabilities into a single model through simple arithmetic in the weight space, withou…
Rethinking VLMs and LLMs for Image Classification
Avi Cooper, Keizo Kato, Chia-Hsien Shih +8
Visual Language Models (VLMs) are now increasingly being merged with Large Language Models (LLMs) to enable new capabilities, particularly in terms of improved interactivity and op…
Configural processing as an optimized strategy for robust object recognition in neural networks
Hojin Jang, Pawan Sinha, Xavier Boix
Configural processing, the perception of spatial relationships among an object's components, is crucial for object recognition. However, the teleology and underlying neurocomputati…