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DTSTAMP:20240116T191657Z
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DTSTART;TZID=America/Denver:20231114T100000
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UID:submissions.supercomputing.org_SC23_sess289_spostu115@linklings.com
SUMMARY:A Comparison of Deep and Shallow Residual Networks for Medical Ima
 ging Classification
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nElaine Lu (Columbi
 a University)\n\nThe complexity and parameters of mainstream large models 
 are increasing rapidly. For example, the increasingly popular large langua
 ge models (e.g., ChatGPT) have billions of parameters. While this has led 
 to performance improvements, the performance gains for simple tasks may be
  unacceptable for the additional cost. We apply residual networks of three
  different depths and evaluate them extensively on the MedMNIST pneumonia 
 dataset. Experimental results show that smaller models can achieve satisfa
 ctory performance at significantly lower costs than larger models.\n\nRegi
 stration Category: Tech Program Reg Pass, Exhibits Reg Pass
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