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09/08/2023
Connecting NTK and NNGP: A Unified Theoretical Framework for Neural Network Learning Dynamics in the Kernel Regime
Artificial neural networks have revolutionized machine learning in recen...
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10/31/2022
Globally Gated Deep Linear Networks
Recently proposed Gated Linear Networks present a tractable nonlinear ne...
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06/14/2022
A theory of learning with constrained weight-distribution
A central question in computational neuroscience is how structure determ...
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05/28/2022
Temporal support vectors for spiking neuronal networks
When neural circuits learn to perform a task, it is often the case that ...
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04/14/2022
Optimal quadratic binding for relational reasoning in vector symbolic neural architectures
Binding operation is fundamental to many cognitive processes, such as co...
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03/14/2022
Soft-margin classification of object manifolds
A neural population responding to multiple appearances of a single objec...
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12/07/2020
Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Renormalization Group
The success of deep learning in many real-world tasks has triggered an e...
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08/19/2020
A new role for circuit expansion for learning in neural networks
Many sensory pathways in the brain rely on sparsely active populations o...
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04/02/2020
Predicting the outputs of finite networks trained with noisy gradients
A recent line of studies has focused on the infinite width limit of deep...
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10/17/2017
Classification and Geometry of General Perceptual Manifolds
Perceptual manifolds arise when a neural population responds to an ensem...
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05/28/2017
Learning Data Manifolds with a Cutting Plane Method
We consider the problem of classifying data manifolds where each manifol...
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05/03/2017
Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity
Neurons and networks in the cerebral cortex must operate reliably despit...
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12/06/2015
Linear Readout of Object Manifolds
Objects are represented in sensory systems by continuous manifolds due t...
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10/26/2010
