Training Algorithm in Neural Networks

Convolutional Neural Network Improves MS Lesion Segmentation

FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, ...

Microsoft

Synthesizing Collective Communication Algorithms for Heterogeneous Networks with TACCL

Large ML models and datasets have necessitated the use of multi-GPU systems for distributed model training. To harness the power offered by multi-GPU systems, it is critical to eliminate bottlenecks ...

Morning Overview on MSN

Your brain has a learning shortcut that AI still can’t copy

Human brains can turn a single messy experience into a lasting skill, while even the most advanced artificial intelligence ...

What is the Difference Between Deep Learning and Machine Learning

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

IEEE

Algorithm Design for Continual Learning in IoT Networks

Abstract: Continual learning (CL) is a new online learning technique over sequentially generated streaming data from different tasks, aiming to maintain a small forgetting loss on previously-learned ...

IEEE

Graph Neural Network Algorithm Based on Graph Convolution and Attention Mechanism

Abstract: With the wide application of graph neural network (GNN) in many fields, how to extract and aggregate node features effectively has become a hot research issue. In this paper, we propose a ...

eLife

Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models

The Virtual Brain Inference (VBI) toolkit enables efficient, accurate, and scalable Bayesian inference over whole-brain network models, improving parameter estimation, uncertainty quantification, and ...