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Found 10 papers, 2 papers with code
Extracting Text Representations for Terms and Phrases in Technical Domains
Extracting dense representations for terms and phrases is a task of great importance for knowledge discovery platforms targeting highly-technical fields.
Unsupervised Term Extraction for Highly Technical Domains
Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains requiring in-depth expertise are scarce and expensive to obtain.
pNLP-Mixer: an Efficient all-MLP Architecture for Language
Large pre-trained language models based on transformer architecture have drastically changed the natural language processing (NLP) landscape.
Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks
The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems.
Scalable Deployment of AI Time-series Models for IoT
A knowledge-based approach to managing model and time-series data allows the use of general semantic concepts for expressing feature engineering tasks.
Castor: Contextual IoT Time Series Data and Model Management at Scale
The main features of Castor are: (1) an efficient pipeline for ingesting IoT time series data in real time; (2) a scalable, hybrid data management service for both time series and contextual data; (3) a versatile semantic model for contextual information which can be easily adopted to different application domains; (4) an abstract framework for developing and storing predictive models in R or Python; (5) deployment services which automatically train and/or score predictive models upon user-defined conditions.
Computation Other Statistics
Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale
The growing complexity of the power grid, driven by increasing share of distributed energy resources and by massive deployment of intelligent internet-connected devices, requires new modelling tools for planning and operation.
Learning Correlation Space for Time Series
We propose an approximation algorithm for efficient correlation search in time series data.
Power Systems Data Fusion based on Belief Propagation
The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices re- quires novel tools for providing a unified and consistent view of the system.
