Formal Concept Lattices
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
arXiv:2606.05471v1 Announce Type: new Abstract: Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this by representing classes through meaningful semantic abstractions, but typically treat all concepts as a flat, unstructured set learned at a single neural network layer. This overlooks a fundamental property of human semantic understanding: concepts being organized hierarchically, from general...
A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders
Announce Type: new Abstract: We propose a unified mathematical framework for a geometric understanding of concept learning and neuron interpretation in sparse autoencoders (SAEs). While SAEs improve interpretability of neural networks by learning sparse feature representations, a principled definition of ''concept'' and ''learning'' remains unclear. We formalize concepts as sets of data points and cast concept learning as a set-alignment problem between human-defined and model-induced concepts.
Crystal Nights by Greg Egan
Publication history - Interzone #215, April 2008. - Free podcast at Transmissions From Beyond. [Site no longer active] - Oceanic (collection, Orion) -