VC Dimensions of Group Convolutional Neural Networks

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

VC Dimension of Neural Networks

This paper presents a brief introduction to Vapnik-Chervonenkis (VC) dimension, a quantity which characterizes the difficulty of distribution-independent learning. The paper establishes various elementary results, and discusses how to estimate the VC dimension in several examples of interest in neural network theory.

متن کامل

Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks

The past few years have witnessed growth in the size and computational requirements for training deep convolutional neural networks. Current approaches parallelize the training process onto multiple devices by applying a single parallelization strategy (e.g., data or model parallelism) to all layers in a network. Although easy to reason about, this design results in suboptimal runtime performan...

متن کامل

Cystoscopy Image Classication Using Deep Convolutional Neural Networks

In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...

متن کامل

Group Equivariant Convolutional Networks

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. By convolving over groups larger than the translation group, G-CNNs build representations that are equivariant to these groups, which makes it possible to greatly increase the degree of parameter sharing. We sho...

متن کامل

Convolutional Neural Networks

Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible. To understand this approximate invertibility phenomenon and how to leverage it more effectively, we focus on a theoretical explanation and develop a mathematical model of sparse signal recovery that is consistent with CNNs with random weights. We give an exact connection to a par...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Social Science Research Network

سال: 2023

ISSN: ['1556-5068']

DOI: https://doi.org/10.2139/ssrn.4331175