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Found 13 papers, 6 papers with code
RUAD: unsupervised anomaly detection in HPC systems
However, current state-of-the-art (SoA) approaches to anomaly detection are supervised and semi-supervised, so they require a human-labelled dataset with anomalies - this is often impractical to collect in production HPC systems.
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp.
Deep Learning for Virus-Spreading Forecasting: a Brief Survey
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes.
A Machine Learning Approach to Online Fault Classification in HPC Systems
We introduce a high-level, easy-to-use fault injection tool called FINJ, with a focus on the management of complex experiments.
BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients.
An Analysis of Regularized Approaches for Constrained Machine Learning
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge.
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey
Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets.
Injective Domain Knowledge in Neural Networks for Transprecision Computing
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets.
Combining Learning and Optimization for Transprecision Computing
The ML model learns the relation between variables precision and the output error; this information is then embedded in the MP focused on minimizing the number of bits.
Distributed, Parallel, and Cluster Computing
Online Anomaly Detection in HPC Systems
Reliability is a cumbersome problem in High Performance Computing Systems and Data Centers evolution.
Distributed, Parallel, and Cluster Computing
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components.
Online Fault Classification in HPC Systems through Machine Learning
As High-Performance Computing (HPC) systems strive towards the exascale goal, studies suggest that they will experience excessive failure rates.
Distributed, Parallel, and Cluster Computing
FINJ: A Fault Injection Tool for HPC Systems
We present FINJ, a high-level fault injection tool for High-Performance Computing (HPC) systems, with a focus on the management of complex experiments.
Distributed, Parallel, and Cluster Computing
