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Found 9 papers, 6 papers with code
Spectral Analysis of Representational Similarity with Limited Neurons
Measuring representational similarity between neural recordings and computational models is challenging due to constraints on the number of neurons that can be recorded simultaneously.
Estimating Neural Representation Alignment from Sparsely Sampled Inputs and Features
In both artificial and biological systems, the centered kernel alignment (CKA) has become a widely used tool for quantifying neural representation similarity.
Statistical Mechanics of Support Vector Regression
A key problem in deep learning and computational neuroscience is relating the geometrical properties of neural representations to task performance.
A Spectral Theory of Neural Prediction and Alignment
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 biological systems.
Bandwidth Enables Generalization in Quantum Kernel Models
Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings.
Out-of-Distribution Generalization in Kernel Regression
Here, we study generalization in kernel regression when the training and test distributions are different using methods from statistical physics.
BIG-bench Machine Learning
Out-of-Distribution Generalization
+1
Asymptotics of representation learning in finite Bayesian neural networks
However, our theoretical understanding of how the learned hidden layer representations of finite networks differ from the fixed representations of infinite networks remains incomplete.
Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks
We present applications of our theory to real and synthetic datasets, and for many kernels including those that arise from training deep neural networks in the infinite-width limit.
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
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 processes and statistical physics.
