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Matthew Baugh

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2 papers

2 author rows

NeSy Conference 2025 Conference Paper

Disentangling Neural Disjunctive Normal Form Models

Kexin Gu Baugh
Vincent Perreault
Matthew Baugh
Luke Dickens
Katsumi Inoue
Alessandra Russo

Neural Disjunctive Normal Form (DNF) based models are powerful and interpretable approaches to neuro-symbolic learning and have shown promising results in classification and reinforcement learning settings without prior knowledge of the tasks. However, their performance is degraded by the thresholding of the post-training symbolic translation process. We show here that part of the performance degradation during translation is due to its failure to disentangle the learned knowledge represented in the form of the networks’ weights. We address this issue by proposing a new disentanglement method; by splitting nodes that encode nested rules into smaller independent nodes, we are able to better preserve the models’ performance. Through experiments on binary, multiclass, and multilabel classification tasks (including those requiring predicate invention), we demonstrate that our disentanglement method provides compact and interpretable logical representations for the neural DNF-based models, with performance closer to that of their pre-translation counterparts. Our code is available at https: //github. com/kittykg/disentangling-ndnf-classification.

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AAAI Conference 2024 Conference Paper

Trade-Offs in Fine-Tuned Diffusion Models between Accuracy and Interpretability

Mischa Dombrowski
Hadrien Reynaud
Johanna P. Müller
Matthew Baugh
Bernhard Kainz

Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning re-search, with many adopting the strategy of fine-tuning pre-trained models using domain-specific text-to-image datasets. Notably, this method has been readily employed for medical applications, such as X-ray image synthesis, leveraging the plethora of associated radiology reports. Yet, a prevailing concern is the lack of assurance on whether these models genuinely comprehend their generated content. With the evolution of text conditional image generation, these models have grown potent enough to facilitate object localization scrutiny. Our research underscores this advancement in the critical realm of medical imaging, emphasizing the crucial role of interpretability. We further unravel a consequential trade-off between image fidelity – as gauged by conventional metrics – and model interpretability in generative diffusion models. Specifically, the adoption of learnable text encoders when fine-tuning results in diminished interpretability. Our in-depth exploration uncovers the underlying factors responsible for this divergence. Consequently, we present a set of design principles for the development of truly interpretable generative models. Code is available at https://github.com/MischaD/chest-distillation.

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