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Found 5 papers, 3 papers with code
PiShield: A NeSy Framework for Learning with Requirements
Given the widespread application of deep learning, there is a growing need for frameworks allowing for the integration of the requirements across various domains.
Exploiting T-norms for Deep Learning in Autonomous Driving
Deep learning has been at the core of the autonomous driving field development, due to the neural networks' success in finding patterns in raw data and turning them into accurate predictions.
How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data
Further, we show how our CL does not necessarily need to be integrated at training time, as it can be also used as a guardrail at inference time, still producing some improvements in the overall performance of the models.
ROAD-R: The Autonomous Driving Dataset with Logical Requirements
Neural networks have proven to be very powerful at computer vision tasks.
Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds
Additionally, we show that it can be deployed on partial scans of objects in a real-world pipeline to improve the outputs of a 3D object detector.
