2 citations · 3 across the 14 of their papers we have counts for
15 papers
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…
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…
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…
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…
Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness
Shreya Saha, Shurui Li, Greta Tuckute +5
The human language system represents both linguistic forms and meanings, but the abstractness of the meaning representations remains debated. Here, we searched for abstract represe…
Seeing Through Words, Speaking Through Pixels: Deep Representational Alignment Between Vision and Language Models
Zoe Wanying He, Sean Trott, Meenakshi Khosla
Recent studies show that deep vision-only and language-only models--trained on disjoint modalities--nonetheless project their inputs into a partially aligned representational space…
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…
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…
Brain-Model Evaluations Need the NeuroAI Turing Test
Jenelle Feather, Meenakshi Khosla, N. Apurva Ratan Murty +1
What makes an artificial system a good model of intelligence? The classical test proposed by Alan Turing focuses on behavior, requiring that an artificial agent's behavior be indis…
Bridging Critical Gaps in Convergent Learning: How Representational Alignment Evolves Across Layers, Training, and Distribution Shifts
Chaitanya Kapoor, Sudhanshu Srivastava, Meenakshi Khosla
Understanding convergent learning -- the degree to which independently trained neural systems -- whether multiple artificial networks or brains and models -- arrive at similar inte…
Evaluating Representational Similarity Measures from the Lens of Functional Correspondence
Yiqing Bo, Ansh Soni, Sudhanshu Srivastava +1
Neuroscience and artificial intelligence (AI) both face the challenge of interpreting high-dimensional neural data, where the comparative analysis of such data is crucial for revea…
Modeling the Human Visual System: Comparative Insights from Response-Optimized and Task-Optimized Vision Models, Language Models, and different Readout Mechanisms
Shreya Saha, Ishaan Chadha, Meenakshi Khosla
Over the past decade, predictive modeling of neural responses in the primate visual system has advanced significantly, largely driven by various DNN approaches. These include model…