A survey on distributed machine learning
The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …
growth has been fueled by advances in machine learning techniques and the ability to …
A survey on ensemble learning for data stream classification
Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …
classification. Their popularity is attributable to their good performance in comparison to …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
6G wireless systems: A vision, architectural elements, and future directions
Internet of everything (IoE)-based smart services are expected to gain immense popularity in
the future, which raises the need for next-generation wireless networks. Although fifth …
the future, which raises the need for next-generation wireless networks. Although fifth …
Distributed learning of deep neural network over multiple agents
In domains such as health care and finance, shortage of labeled data and computational
resources is a critical issue while develo** machine learning algorithms. To address the …
resources is a critical issue while develo** machine learning algorithms. To address the …
Privacy-preserving deep learning
Deep learning based on artificial neural networks is a very popular approach to modeling,
classifying, and recognizing complex data such as images, speech, and text. The …
classifying, and recognizing complex data such as images, speech, and text. The …
Machine learning for streaming data: state of the art, challenges, and opportunities
Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …
associated with learning algorithms that update their models given a continuous influx of …
Bag of tricks for efficient text classification
This paper explores a simple and efficient baseline for text classification. Our experiments
show that our fast text classifier fastText is often on par with deep learning classifiers in terms …
show that our fast text classifier fastText is often on par with deep learning classifiers in terms …
Compressing neural networks with the hashing trick
As deep nets are increasingly used in applications suited for mobile devices, a fundamental
dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever …
dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever …
Cirrus: A serverless framework for end-to-end ml workflows
Machine learning (ML) workflows are extremely complex. The typical workflow consists of
distinct stages of user interaction, such as preprocessing, training, and tuning, that are …
distinct stages of user interaction, such as preprocessing, training, and tuning, that are …