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Linking Sparse Coding Dictionaries for Representation Learning
Conference proceeding

Linking Sparse Coding Dictionaries for Representation Learning

Nicki Barari and Edward Kim
2021 International Conference on Rebooting Computing (ICRC), pp 84-87
Nov 2021
DOI: https://doi.org/10.1109/ICRC53822.2021.00022

Abstract

Aerospace electronics Classification Dictionaries Learning (artificial intelligence) Linear programming machine learning neuro-inspired artificial intelligence Pipelines Representation learning Supervised learning
Sparsity is a desirable property as our natural environment can be described by a small number of structural primitives. Strong evidence demonstrates that the brain's representation is both explicit and sparse, which makes it metabolically efficient by reducing the cost of code transmission. In current standardized machine learning practices, end-to-end classification pipelines are much more prevalent. For the brain, there is no single classification objective function optimized by back-propagation. Instead, the brain is highly modular and learns based on local information and learning rules. In our work, we seek to show that an unsupervised, biologically inspired sparse coding algorithm can create a sparse representation that achieves a classification accuracy on par with standard supervised learning algorithms. We leverage the concept of multi-modality to show that we can link the embedding space with multiple, heterogeneous modalities. Furthermore, we demonstrate a sparse coding model which controls the latent space and creates a sparse disentangled representation, while maintaining a high classification accuracy.

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1 citations in Web of Science
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Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Quantum Science & Technology
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