4 citations · 7 across the 5 of their papers we have counts for
6 papers
Representational Difference Explanations
Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona
We propose a method for discovering and visualizing the differences between two learned representations, enabling more direct and interpretable model comparisons. We validate our m…
Representational Similarity via Interpretable Visual Concepts
Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona
How do two deep neural networks differ in how they arrive at a decision? Measuring the similarity of deep networks has been a long-standing open question. Most existing methods pro…
A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification
Markus Marks, Manuel Knott, Neehar Kondapaneni +4
Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn abo…
Text-image Alignment for Diffusion-based Perception
Neehar Kondapaneni, Markus Marks, Manuel Knott +2
Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. Ho…
Visual Knowledge Tracing
Neehar Kondapaneni, Pietro Perona, Oisin Mac Aodha
Each year, thousands of people learn new visual categorization tasks -- radiologists learn to recognize tumors, birdwatchers learn to distinguish similar species, and crowd workers…
A Number Sense as an Emergent Property of the Manipulating Brain
Neehar Kondapaneni, Pietro Perona
The ability to understand and manipulate numbers and quantities emerges during childhood, but the mechanism through which humans acquire and develop this ability is still poorly un…