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Composing Features: Compositional Model Augmentation for Steerability of Music Transformers
To tackle the combinatorial nature of composing features, we propose a compositional approach to steering music transformers, building on lightweight fine-tuning methods such as prefix tuning and bias tuning.
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints
We propose a novel approach for incorporating structure in the form of relational constraints between different subcomponents of an example (e. g., lines of a poem or measures of music).
Learning Neurosymbolic Generative Models via Program Synthesis
Significant strides have been made toward designing better generative models in recent years.
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