Mar 8, 2021 · We propose to use non-convex T \ell _{1} regularizer along with the additional effect of sparse group lasso to completely remove the redundant neurons/filters.
Apr 24, 2020 · In this paper, a nonconvex regularization method is investigated in the context of shallow ReLU networks.
Many currently employed neural network training procedures are based on minimizing nonconvex functionals, and employ random initialization and stochastic ...
We propose to use the structured sparsity regularization, which learns the com- pressed network architecture based on the sparse group lasso and transformed 1 ...
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE. 1. Learning Sparse Neural Networks Using. Non-Convex Regularization. 1. 2. Mohammad Khalid ...
Jan 14, 2022 · Learning sparse neural networks through l 0 regularization. In. Proceedings of the International Conference on Learning Representations.
Mar 26, 2020 · In this paper, we propose a novel penalized estimation method for sparse DNNs, which resolves the aforementioned problems existing in the sparsity constraint.
Mar 10, 2021 · DNNs owe its success to the presence of large number of weight parameters (and increased depth), which led to huge computation and memory costs ...
Convex ℓ1 regularization using an infinite dictionary of neurons has been suggested for constructing neural networks with desired approximation guarantees, ...
Jan 14, 2022 · In particular, we prove that the sparse-penalized estimator can adaptively attain minimax convergence rates for various nonparametric regression ...