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Found 13 papers, 2 papers with code
Serving Deep Learning Model in Relational Databases
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently.
Effectiveness and predictability of in-network storage cache for scientific workflows
We also build a machine learning model to study the predictability of the cache behavior.
Feature Engineering and Classification Models for Partial Discharge in Power Transformers
These features represent the entire signal and not just a single phase, so the feature set has a fixed size and is easily comprehensible.
Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow
In this work, we describe a number of techniques to extract dynamic information about the current state of a large scientific workflow, which could be generalized to other types of applications.
Access Trends of In-network Cache for Scientific Data
Our study shows that this distributed storage cache is able to reduce the network traffic volume by a factor of 2. 35 during a part of the study period.
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers
We address these challenges with Adaptive SGD, an adaptive elastic model averaging stochastic gradient descent algorithm for heterogeneous multi-GPUs that is characterized by dynamic scheduling, adaptive batch size scaling, and normalized model merging.
Improving Botnet Detection with Recurrent Neural Network and Transfer Learning
Botnet detection is a critical step in stopping the spread of botnets and preventing malicious activities.
Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data
Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns, which can reveal important heterogeneity across different time scales, weather conditions, as well as within and across individual households.
Deep Learning for Surface Wave Identification in Distributed Acoustic Sensing Data
Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology.
Deep Learning on Real Geophysical Data: A Case Study for Distributed Acoustic Sensing Research
Deep Learning approaches for real, large, and complex scientific data sets can be very challenging to design.
ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management
We present ADIOS 2, the latest version of the Adaptable Input Output (I/O) System.
Botnet Detection Using Recurrent Variational Autoencoder
Botnets are increasingly used by malicious actors, creating increasing threat to a large number of internet users.
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting.
