AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Concept-based vision models represent images through an intermediate layer of human-inspectable concepts, so what a model relies on can be traced to those concepts. However, those models are often limited to fixed categories or depend on language to define their concepts. We introduce DisParQ (Discrete Parts with Quant...
Concept bottleneck models (CBMs) make predictions inspectable and intervenable by routing them through human-interpretable concepts, but originally required concept annotations. Annotation-free variants remove this requirement, but typically use large concept vocabularies, static at both training and inference, produci...
This work proposes TEVI, a framework that uses captions as a signal for what to retain from image embeddings and uses sparse autoencoders to disentangle image embeddings and train a masking module to selectively reconstruct the embedding based on a given caption.
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