GDNN is a Python (with a bit of C++) library for training deep neural networks with minibatch stochastic gradient descent with momentum (hereafter called SGD) and backpropagation. …

We derive an efficient implementation of G -DNNs after a reparameterization of weights, as well as necessary and sufficient conditions for an architecture to be "admissible"-- i.e., …

Code implementation for : Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (AAAI'21) # run after installing correct Pytorch package. bash install.sh. Run to check if …

Experiments for speaker consistency modeling by using a classification network to help generate gender-dependent phonetic probabilities for a statistical recognition system are reported. …

2022年8月15日 · In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media.

2025年3月10日 · This paper introduces a novel approach to solving time-varying quadratic programming (TVQP) problems with time-dependent constraints, using gradient-based …

2023年12月19日 · In the Generalized Deep Neural Network (GDNN), the traditional notion of neurons in each layer is replaced by a continuous state function. Using the finite rank …

Applying this method to gated dynamic NN (GDNN), the adaptive inference pathway-gated NN (AIPGNN) model is proposed. The AIPGNN is capable of adaptively activating specific finite …

2021年4月30日 · In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the …

In this article, we present a gated dynamic neural network (GDNN) DPD model to linearize the power amplifier (PA) with varying transmission configurations. The proposed GDNN model is …