HypCBM reformulates concept activations as geometric containment in hyperbolic space to produce sparse, hierarchy-aware signals that match Euclidean models trained on 20 times more data.
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IPL alternates discrete semantic token selection using approximate submodular optimization with continuous prompt optimization to boost both interpretability and task performance in vision-language model adaptation.
Gyan is a novel explainable non-transformer language model that achieves SOTA results on multiple datasets by mimicking human-like compositional context and world models.
citing papers explorer
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Hyperbolic Concept Bottleneck Models
HypCBM reformulates concept activations as geometric containment in hyperbolic space to produce sparse, hierarchy-aware signals that match Euclidean models trained on 20 times more data.
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Joint Semantic Token Selection and Prompt Optimization for Interpretable Prompt Learning
IPL alternates discrete semantic token selection using approximate submodular optimization with continuous prompt optimization to boost both interpretability and task performance in vision-language model adaptation.
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Gyan: An Explainable Neuro-Symbolic Language Model
Gyan is a novel explainable non-transformer language model that achieves SOTA results on multiple datasets by mimicking human-like compositional context and world models.