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Found 10 papers, 0 papers with code
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology available to every developer.
Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms
In contrast, in Griffin we cast the problem to a corresponding regression one that predicts the runtime of a job, and show how the relative contributions of the features used to train our interpretable model can be exploited to rank the potential causes of job slowdowns.
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Consequently, rigorous data management has emerged as a key requirement in enterprise settings.
Data Science through the looking glass and what we found there
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners.
Vamsa: Automated Provenance Tracking in Data Science Scripts
There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requirements of various ML applications.
MLOS: An Infrastructure for Automated Software Performance Engineering
In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering.
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation
Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost.
Phoebe: A Learning-based Checkpoint Optimizer
To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer.
Homotopy Based Reinforcement Learning with Maximum Entropy for Autonomous Air Combat
In this paper, we propose a homotopy-based soft actor-critic method (HSAC) which focuses on addressing these problems via following the homotopy path between the original task with sparse reward and the auxiliary task with artificial prior experience reward.
Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition
In the LTS-GAT, a divide-and-conquer scheme was used to examine local information on temporal and spatial dimensions of EEG patterns based on the graph attention mechanism.
