1 citations · 2 across the 5 of their papers we have counts for
5 papers
Exploring Curriculum Learning for Vision-Language Tasks: A Study on Small-Scale Multimodal Training
Rohan Saha, Abrar Fahim, Alona Fyshe +1
For specialized domains, there is often not a wealth of data with which to train large machine learning models. In such limited data / compute settings, various methods exist aimin…
It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap
Abrar Fahim, Alex Murphy, Alona Fyshe
Multi-modal contrastive models such as CLIP achieve state-of-the-art performance in zero-shot classification by embedding input images and texts on a joint representational space.…
Correcting Biased Centered Kernel Alignment Measures in Biological and Artificial Neural Networks
Alex Murphy, Joel Zylberberg, Alona Fyshe
Centred Kernel Alignment (CKA) has recently emerged as a popular metric to compare activations from biological and artificial neural networks (ANNs) in order to quantify the alignm…
Finding Shared Decodable Concepts and their Negations in the Brain
Cory Efird, Alex Murphy, Joel Zylberberg +1
Prior work has offered evidence for functional localization in the brain; different anatomical regions preferentially activate for certain types of visual input. For example, the f…
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models
Cory Efird, Alex Murphy, Joel Zylberberg +1
We introduce a method that takes advantage of high-quality pretrained multimodal representations to explore fine-grained semantic networks in the human brain. Previous studies have…