Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Network… ...

Abstract: Large amount of redundancy is widely present in convolutional neural networks (CNNs). Identifying the redundancy ... we achieve a 57.1% FLOPs reduction with no accuracy drop on ImageNet, ...

Researchers have explored the potential of deep neural networks (DNNs) in transforming fragrance design. By analyzing the sensing data of 180 essential oils, the DNN was trained using the odor ...

A three-block temporal convolutional neural network (3B-TCN ... which generates 30,000 sets of data to train the 3B-TCN. The SSVEF signal classification accuracies of the 3B-TCN network are 96.61%, 92 ...

As is typical, the dispute centers around fees that Optimum's parent company Altice USA must pay for the regional sports networks. As is typical, the dispute centers around fees that Optimum's parent ...

U.S. telecom giants AT&T and Verizon say they have secured their networks after being targeted by the China-linked Salt Typhoon cyberespionage group. In a statement given to TechCrunch on Monday ...

AI models like artificial neural networks and language models help scientists solve ... With the 2018 release of AlphaFold, an AI deep learning model, scientists were finally able to predict the 3D ...

Earth is an ocean planet, with over 70 percent of its surface covered in seas. With deep-sea robots, scientists regularly reveal new insights into the most mysterious realms of these expansive waters.

These parameters were subsequently used as input to a model that integrates the Convolutional Block Attention Module and a Long Short-Term Memory neural network ... approach to enhance classification ...

It is a neural network made up of multiple independent deep neural networks which acts as modules working on segments of a problem. Can be used to support use cases like image classification. Neural ...