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Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning
In this paper, we show that by combining a neural-symbolic system with methods from continual learning, LTN can obtain a higher level of accuracy when addressing non-monotonic reasoning tasks.
Accountability in AI: From Principles to Industry-specific Accreditation
Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes.
A Hybrid Recurrent Neural Network For Music Transcription
We investigate the problem of incorporating higher-level symbolic score-like information into Automatic Music Transcription (AMT) systems to improve their performance.
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