Towards Migrating Neural Network Implementations
An model-driven approach to automatically migrate neural network code across deep learning frameworks.
An model-driven approach to automatically migrate neural network code across deep learning frameworks.
We present a privacy-aware query generation approach that identifies sensitive information in the knowledge graph and masks it before sending anything to the LLM. Our experiments indicate that this preserves query quality while preventing sensitive data from leaving your system.
With the latest release of BESSER, our platform now includes a no-code editor for designing GUIs and dashboards directly within the modeling environment
Smart data models described visually in UML can be directly turned into NGSI-LD compliant data models, drastically reducing the time between design and deployment in real-world systems.
A neural network metamodel and generators to create neural networks in a platform-independent way
Our proposal for a formalization of the Digital Product Passport concept and a code generator to accelerate the development of applications around the DPP ecosystem.
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