15 papers
Similarity of Neural Network Representations in Superposition
Sunny Liu, Habon Issa, André Longon +4
Comparing internal representations is a central goal in neuroscience and machine learning, but standard linear alignment metrics (Representational Similarity Analysis, Centered Ker…
Representational Alignment Across Model Layers and Brain Regions with Multi-Level Optimal Transport
Shaan Shah, Meenakshi Khosla
Standard representational similarity methods align each layer of a network to its best match in another independently, producing asymmetric results, lacking a global alignment scor…
Geometry-Aware CLIP Retrieval via Local Cross-Modal Alignment and Steering
Nirmalendu Prakash, Narmeen Fatimah Oozeer, Xin Su +8
CLIP retrieval is typically framed as a pointwise similarity problem in a shared embedding space. While CLIP achieves strong global cross-modal alignment, many retrieval failures a…
Integrated representational signatures strengthen specificity in brains and models
Jialin Wu, Shreya Saha, Yiqing Bo +1
The extent to which different neural or artificial neural networks (models) rely on equivalent representations to support similar tasks remains a central question in neuroscience a…
Partial Soft-Matching Distance for Neural Representational Comparison with Partial Unit Correspondence
Chaitanya Kapoor, Alex H. Williams, Meenakshi Khosla
Representational similarity metrics typically force all units to be matched, making them susceptible to noise and outliers common in neural representations. We extend the soft-matc…
Comparing and Integrating Different Notions of Representational Correspondence in Neural Systems
Jialin Wu, Shreya Saha, Yiqing Bo +1
The extent to which different biological and artificial neural systems rely on equivalent internal representations to support similar tasks remains a central question in neuroscien…