2 citations · 3 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…
Barycentric alignment for instance-level comparison of neural representations
Shreya Saha, Zoe Wanying He, Meenakshi Khosla
Comparing representations across neural networks is challenging because representations admit symmetries, such as arbitrary reordering of units or rotations of activation space, th…
Superposition disentanglement of neural representations reveals hidden alignment
André Longon, David Klindt, Meenakshi Khosla
The superposition hypothesis states that single neurons may participate in representing multiple features in order for the neural network to represent more features than it has neu…
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…
Measuring the Measures: Discriminative Capacity of Representational Similarity Metrics Across Model Families
Jialin Wu, Shreya Saha, Yiqing Bo +1
Representational similarity metrics are fundamental tools in neuroscience and AI, yet we lack systematic comparisons of their discriminative power across model families. We introdu…