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cs.LG2025
Concept-Guided Interpretability via Neural Chunking
Shuchen Wu, Stephan Alaniz, Shyamgopal Karthik +3
Neural networks are often described as black boxes, reflecting the significant challenge of understanding their internal workings and interactions. We propose a different perspecti…
cs.LG2025
Learning Patterns and Abstractions from Perceptual Sequences
Shuchen Wu
Cognition swiftly breaks high-dimensional sensory streams into familiar parts and uncovers their relations. Why do structures emerge, and how do they enable learning, generalizatio…
cs.LG2025
Discovering Chunks in Neural Embeddings for Interpretability
Shuchen Wu, Stephan Alaniz, Eric Schulz +1
Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it…