106 citations · 167 across the 9 of their papers we have counts for
10 papers
Estimating Dimensionality of Neural Representations from Finite Samples
Chanwoo Chun, Abdulkadir Canatar, SueYeon Chung +1
The global dimensionality of a neural representation manifold provides rich insight into the computational process underlying both artificial and biological neural networks. Howeve…
Estimating Neural Representation Alignment from Sparsely Sampled Inputs and Features
Chanwoo Chun, Abdulkadir Canatar, SueYeon Chung +1
In both artificial and biological systems, the centered kernel alignment (CKA) has become a widely used tool for quantifying neural representation similarity. While current CKA est…
Spectral Analysis of Representational Similarity with Limited Neurons
Hyunmo Kang, Abdulkadir Canatar, SueYeon Chung
Understanding representational similarity between neural recordings and computational models is essential for neuroscience, yet remains challenging to measure reliably due to the c…
Statistical Mechanics of Support Vector Regression
Abdulkadir Canatar, SueYeon Chung
A key problem in deep learning and computational neuroscience is relating the geometrical properties of neural representations to task performance. Here, we consider this problem f…
A Spectral Theory of Neural Prediction and Alignment
Abdulkadir Canatar, Jenelle Feather, Albert Wakhloo +1
The representations of neural networks are often compared to those of biological systems by performing regression between the neural network responses and those measured from biolo…
Bandwidth Enables Generalization in Quantum Kernel Models
Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan +2
Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings. For example, quantum kernel methods have been…
Out-of-Distribution Generalization in Kernel Regression
Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan
In real word applications, data generating process for training a machine learning model often differs from what the model encounters in the test stage. Understanding how and wheth…
Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks
Abdulkadir Canatar, Blake Bordelon, Cengiz Pehlevan
Generalization beyond a training dataset is a main goal of machine learning, but theoretical understanding of generalization remains an open problem for many models. The need for a…
Strong localization in a suspended monolayer graphene by intervalley scattering
Cenk Yanik, Vahid Sazgari, Abdulkadir Canatar +2
A gate induced insulating behavior at zero magnetic field is observed in a high mobility suspended monolayer graphene near the charge neutrality point. The graphene device initiall…
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan
We derive analytical expressions for the generalization performance of kernel regression as a function of the number of training samples using theoretical methods from Gaussian pro…