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Found 7 papers, 1 papers with code
A modular framework for extreme weather generation
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change.
Workflow Provenance in the Lifecycle of Scientific Machine Learning
We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs.
Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation
In this paper, we present our position for a neuralsymbolic integration strategy, arguing in favor of a hybrid representation to promote an effective integration.
Managing Machine Learning Workflow Components
To handle this problem, in this paper, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation.
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary.
Semantic Segmentation of Seismic Images
We compare our approach with two well-known deep neural network topologies: Fully Convolutional Network and U-Net.
Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation
In this work, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation.
