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Found 20 papers, 9 papers with code
Exploring Error Bits for Memory Failure Prediction: An In-Depth Correlative Study
In this paper, we present a comprehensive study on the correlation between CEs and UEs, specifically emphasizing the importance of spatio-temporal error bit information.
Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself.
Individualized prescriptive inference in ischaemic stroke
The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials.
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.
Leveraging Log Instructions in Log-based Anomaly Detection
Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating large IT systems using diverse AI-enabled methods and tools for, e. g., anomaly detection and root cause analysis, to support the remediation, optimization, and automatic initiation of self-stabilizing IT activities.
Data-Driven Approach for Log Instruction Quality Assessment
We start with an in-depth analysis of quality log instruction properties in nine software systems and identify two quality properties: 1) correct log level assignment assessing the correctness of the log level, and 2) sufficient linguistic structure assessing the minimal richness of the static text necessary for verbose event description.
Failure Identification from Unstable Log Data using Deep Learning
Our experimental results demonstrate that the learned subprocesses representations reduce the instability in the input, allowing CLog to outperform the baselines on the failure identification subproblems - 1) failure detection by 9-24% on F1 score and 2) failure type identification by 7% on the macro averaged F1 score.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations.
IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environment
A fault or an anomaly in the VMM can propagate to the VMs hosted on it and ultimately affect the availability and reliability of the applications running on those VMs.
Neuradicon: operational representation learning of neuroimaging reports
Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis.
Online Memory Leak Detection in the Cloud-based Infrastructures
A memory leak in an application deployed on the cloud can affect the availability and reliability of the application.
Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper
Finally, all this will result in faster adoption of AIOps, further increase the interest in this research field and contribute to bridging the gap towards fully-autonomous operating IT systems.
A Systematic Mapping Study in AIOps
IT systems of today are becoming larger and more complex, rendering their human supervision more difficult.
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions.
Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs
We propose Logsy, a classification-based method to learn log representations in a way to distinguish between normal data from the system of interest and anomaly samples from auxiliary log datasets, easily accessible via the internet.
Self-Supervised Log Parsing
This allows the coupling of the MLM as pre-training with a downstream anomaly detection task.
Machine learning and glioma imaging biomarkers
Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).
Knowledge distillation for semi-supervised domain adaptation
As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
