Public health initiatives must be made using evidence-based decision-making to have the
greatest impact. Machine learning algorithms are created to gather, store, process, and …

When using distributed machine learning (ML) systems to train models on a cluster of worker
machines, users must configure a large number of parameters: hyper-parameters (eg the …

Deep Learning Recommender Systems (DLRSs) need to update models at low latency, thus
promptly serving new users and content. Existing DLRSs, however, fail to do so. They …

The study's objective is to predict the environmental, social, and governance (ESG) ratings
of firms. Applying six machine learning algorithms, we collect a global data sample of 6166 …

Convolutional neural networks (CNNs) have proven to be very successful in learning task-
specific computer vision features. To integrate features from different layers in standard …

Estimating human pose is an important yet challenging task in multimedia applications.
Existing pose estimation libraries target reproducing standard pose estimation algorithms …

Medical imaging deep learning models are often large and complex, requiring specialized
hardware to train and evaluate these models. To address such issues, we propose the …

This paper presents MoE-Infinity, a cost-efficient mixture-of-expert (MoE) serving system that
realizes activation-aware expert offloading. MoE-Infinity features sequence-level expert …

To achieve higher utilisation, cloud providers offer VMs with GPUs as lower-cost transient
cloud resources. Transient VMs can be revoked at short notice and vary in their availability …

Systems for serving inference requests on graph neural networks (GNN) must combine low
latency with high throughout, but they face irregular computation due to skew in the number …