Scaling AI with Dynamic Inference Paths in Neural Networks

Introduction

IBM Research, with the help of the University of Texas Austin and the University of Maryland, has tried to expedite the performance of neural networks by creating technology, called BlockDrop. Behind the design of this technology lies the objective and promise of speeding up convolutional neural network operations without…

Use of SecureSVM, Boosting, Bagging, Clustering, LSTM, CNN, GAN in Retail with BlockChain

Introduction

This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. This blog is structured as follows:

• Overview of the role of blockchain in the retail industry.
• Different traditional…

Anomaly Detection from Head and Abdominal Fetal ECG — A Case study of IOT anomaly detection using Generative Adversarial Networks

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal…

Motivation

There are five types of traditional time series models most commonly used in epidemic time series forecasting, which includes

• Autoregressive (AR),
• Moving Average (MA),
• Autoregressive Moving Average (ARMA),
• Autoregressive Integrated Moving Average (ARIMA), and
• Seasonal Autoregressive Integrated Moving Average (SARIMA) models.

AR models express the current value of the time series linearly in…

Traditional vs Deep Learning Algorithms in the Telecom Industry — Cloud Architecture and Algorithm Categorization

Google Cloud Architecture for Machine Learning Algorithms in the Telecom Industry

Introduction

The unprecedented growth of mobile devices, applications, and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research…