Our primary contri- bution lies in the rigorous formulation of these filters via a constrained empirical risk minimization framework, thereby providing an exact ...
Our primary contri-bution lies in the rigorous formulation of these filters via a constrained empirical risk minimization framework, thereby providing an exact ...
scholar.google.com › citations
Our primary contri- bution lies in the rigorous formulation of these filters via a constrained empirical risk minimization framework, thereby providing an exact ...
Feb 9, 2023 · Here we present a framework that, under mild assumptions, allows the exact enforcement of constraints on parameterized sets of functions such as DNNs.
This makes our framework highly flexible and enables us to deal with a vast class of constraints for which we can construct appro- priate (graph) charts and ...
We introduce a Convolutional Neural Network (CNN) wherein the convolutional filters are strictly constrained to be wavelets.
Jun 2, 2024 · In this video we summarize our paper accepted at CVPR 2024. We explain how to train neural networks with constaints on its parameters using ...
Jul 21, 2025 · Principles of Risk Minimization for Learning Theory. Conference Paper. Jan 1991. Vladimir Vapnik.
CERM is a deep learning framework for training neural networks with constraints. Here, we briefly explain how to use the general framework and run the examples.
Apr 8, 2024 · Our latest paper - Task-Driven Wavelets Using Constrained Empirical Risk Minimization - has been accepted at CVPR for an oral presentation.