Search Results for author: Guolong Su

Found 6 papers, 2 papers with code

QueryForm: A Simple Zero-shot Form Entity Query Framework

no code implementations14 Nov 2022 Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.

Transfer Learning

Learning to Prompt for Continual Learning

1 code implementation CVPR 2022 Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister

The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.

Continual Learning Image Classification

Interpretable Two-level Boolean Rule Learning for Classification

no code implementations18 Jun 2016 Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov

As a contribution to interpretable machine learning research, we develop a novel optimization framework for learning accurate and sparse two-level Boolean rules.

BIG-bench Machine Learning Classification +2

Interpretable Two-level Boolean Rule Learning for Classification

no code implementations23 Nov 2015 Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov

Experiments show that the two-level rules can yield noticeably better performance than one-level rules due to their dramatically larger modeling capacity, and the two algorithms based on the Hamming distance formulation are generally superior to the other two-level rule learning methods in our comparison.

Classification General Classification

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