Attention:The NSF Public Access Repository (PAR) system and access will be unavailable from 5:00 PM ET until 8:00 PM ET on Friday, September 11 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
Award ID(s):
2531023
PAR ID:
10631700
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Forty-Second International Conference on Machine Learning
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al (, Nature)
    null (Ed.)
  2. ; ; ; ; ; ; ; ; ; ; et al (, The Astrophysical Journal)
    null (Ed.)
  3. ; ; ; ; ; (, OSA Continuum)
    null (Ed.)
  4. ; ; ; (, The Electronic Journal of Linear Algebra)
    null (Ed.)
    A unified approach to the determination of eigenvalues and eigenvectors of specific matrices associated with directed graphs is presented. Matrices studied include the new distance matrix, with natural extensions to the distance Laplacian and distance signless Laplacian, in addition to the new adjacency matrix, with natural extensions to the Laplacian and signless Laplacian. Various sums of Kronecker products of nonnegative matrices are introduced to model the Cartesian and lexicographic products of digraphs. The Jordan canonical form is applied extensively to the analysis of spectra and eigenvectors. The analysis shows that Cartesian products provide a method for building infinite families of transmission regular digraphs with few distinct distance eigenvalues. 
    more » « less
  5. ; ; ; (, Journal of Pure and Applied Algebra)
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Text Encoded Conversion
  • XML
  • Save / Share this Record
  • Send to Email