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Updated: Mar 31, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200×.

Evangelos E Papalexakis1, Christos Faloutsos1, Tom M Mitchell1

  • 1Carnegie Mellon University.

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|October 17, 2015
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Summary
This summary is machine-generated.

Researchers developed TURBO-SMT to rapidly analyze brain activity and word properties. This new method significantly speeds up coupled matrix-tensor factorization, enabling faster insights into neural responses to language.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Data Science

Background:

  • Correlating human brain neural activity with word properties is challenging.
  • Coupled Matrix-Tensor Factorization (CMTF) is a method used for such analyses.
  • Existing CMTF solvers can be computationally intensive, requiring hours to days.

Purpose of the Study:

  • To accelerate Coupled Matrix-Tensor Factorization (CMTF) solvers.
  • To introduce a meta-method, TURBO-SMT, for performance enhancement of CMTF algorithms.
  • To enable efficient joint analysis of brain activity and word properties.

Main Methods:

  • Development of TURBO-SMT, a meta-method for accelerating CMTF.
  • Application of TURBO-SMT to the BRAINQ dataset, which includes brain activity tensors and word property matrices.
  • Evaluation of TURBO-SMT's performance against baseline CMTF methods.

Main Results:

  • TURBO-SMT achieved up to 200x performance boost for CMTF algorithms.
  • The method demonstrated up to a 65-fold increase in sparsity.
  • Comparable accuracy was maintained compared to baseline methods.
  • Meaningful latent variables were identified, predicting brain activity with competitive accuracy.

Conclusions:

  • TURBO-SMT significantly accelerates CMTF, making complex analyses feasible in minutes.
  • The method effectively extracts latent variables correlating neural activity with semantic properties.
  • TURBO-SMT shows promise for advancing computational neuroscience and machine learning applications.