Papers › VICE: Variational Interpretable Concept Embeddings

VICE: Variational Interpretable Concept Embeddings

2 May 2022arXiv:2205.00756archive 2025-07-28

Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.

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lukasmut/vice officialmentioned in papermentioned on GitHubpytorchGPL-3.0 report
florianmahner/object-dimensions mentioned on GitHubpytorch report

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2ran · our draft was wrong
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create_dirs lukasmut/vice/main_optimization.py official repository ran · our draft was wrong GPL-3.0 (copyleft) · pointer only · 7f55254e852325cf · report
get_nobjects lukasmut/vice/main_optimization.py official repository ran · our draft was wrong fingerprinted GPL-3.0 (copyleft) · pointer only · 30c7e4f6bf8a66f0 · report
LogGaussianPrior florianmahner/object-dimensions/objdim/core/embedding.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted GPL-3.0 (copyleft) · pointer only · e89369453e2a4bdb · report
LogNormalDimensionPruning florianmahner/object-dimensions/objdim/core/embedding.py community (archive-listed) ran GPL-3.0 (copyleft) · pointer only · c502787ca7daadbd · report
QLogVar florianmahner/object-dimensions/objdim/core/embedding.py community (archive-listed) ran · metamorphic tier: deterministic GPL-3.0 (copyleft) · pointer only · dee32d104aaabaf6 · report
QMu florianmahner/object-dimensions/objdim/core/embedding.py community (archive-listed) ran · metamorphic tier: deterministic GPL-3.0 (copyleft) · pointer only · e3ea85ac5b9b4d8f · report
SpikeSlabPrior florianmahner/object-dimensions/objdim/core/embedding.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted GPL-3.0 (copyleft) · pointer only · 621adbedfa83ca45 · report
BasePrior florianmahner/object-dimensions/objdim/core/embedding.py community (archive-listed) unverified GPL-3.0 (copyleft) · pointer only · 268173e9543f7376 · report
NormalDimensionPruning florianmahner/object-dimensions/objdim/core/embedding.py community (archive-listed) unverified GPL-3.0 (copyleft) · pointer only · 39d9c1ddbcab2c30 · report
VariationalEmbedding florianmahner/object-dimensions/objdim/core/embedding.py community (archive-listed) unverified GPL-3.0 (copyleft) · pointer only · 91b0aa78974b09e0 · report

Tasks

Experimental DesignObjectOdd One OutPAC learningVariational Inference

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Variational Inference

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