Theoretical foundations can be found in the following papers. Learning of constraints: @inproceedings{ciravegna2020constraint, title={A Constraint-Based Approach to Learning and Explanation.}, author= ...

Deep reinforcement learning (DRL) has been employed in solving challenging decision-making problems in autonomous driving. Safe decision-making in autonomous highway driving is among the foremost open ...

Digital learning, a form of education that uses electronic resources to facilitate learning, often requires a delicate balance between creativity and the constraints inherent in the medium.

This repository contains two notebooks which will guide you step-by-step towards the implementation of learning of and with constraints in Pytorch. Learning with constraints: learn how to train a NN ...

Phonological processes are context-dependent sound changes in natural languages. We present an unsupervised approach to learning human-readable descriptions of phonological processes from collections ...

Balancing time constraints with hands-on learning requires creative integration of practical activities into the curriculum. Integrate project-based assignments that complement theoretical ...

Abstract: Learning symbolic-level numerical constraints is key to use abstractions in effective reasoning and transfer of knowledge for robot systems. We investigate this problem in an ...

Citation: Friedmann S, Frémaux N, Schemmel J, Gerstner W and Meier K (2013) Reward-based learning under hardware constraints—using a RISC processor embedded in a neuromorphic substrate. Front.