Fatal heart, brain monitoring is important for the mother and the baby as it helps to understand the baby’s growth.Lets understand how we discover anomalies from cyclic patterns
Close
AI Research
April 28, 2020
Leveraging the Present to Anticipate the Future in Videos
Predict future action labels instead of predicting pixel-level information — Summarization of research paper by Facebook AI Motivation Anticipating actions before they are executed are necessary in day to day lives for pedestrians crossing the road or for an athlete going for a high jump or playing volleyball . We have also seen that it serves wide range of practical applications including autonomous driving, robotics and secured system monitoring. Prior work done in this field […]
Neural Architecture Search with NASBench from Google Research— Can we design network architectures automatically, instead of relying on expert experience and knowledge? Motivation Recent advances in neural architecture search (NAS) has vastly increased demand of high-end servers having tremendous computational resources. As the cost of maintaining high-end servers is high, it becomes inaccessible to students and researchers. As a result, it creates a system limitation when students and researchers are not able to reproduce experiments […]
April 27, 2020
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 and development of 5G systems have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experiences challenges due to […]
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 a technology, called BlockDrop. Behind the design of this technology lies the objective and promise of speeding up convolutional neural network operations without any loss of fidelity, which can offer a great savings of cost to the ML […]
