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MAKING PRE-TRAINED LANGUAGE MODELS GREAT ON TABULAR PREDICTION
Jiahuan Yan
, Bo Zheng
, Hongxia Xu
, Yiheng Zhu
, Danny Z. Chen
,
Jimeng Sun
, Jian Wu
, Jintai Chen
Siebel School of Computing and Data Science
Biomedical and Translational Sciences
Coordinated Science Lab
Neuroscience Program
Research output
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Contribution to conference
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Paper
›
peer-review
Overview
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Keyphrases
Feature Value
100%
Deep Neural Network
100%
Tabular Data
100%
Pre-trained Language Model
100%
Data Prediction
66%
Numerical Characteristics
66%
Relative Magnitude
33%
Image Processing
33%
Comprehensive Experiment
33%
Scalar
33%
Language Processing
33%
Transferability
33%
Knowledge Transfer
33%
Gradient Boosting Decision Tree
33%
Text Representation
33%
Language Model
33%
Token
33%
Decision Tree Model
33%
Tokenization
33%
Representation Space
33%
Classification Task
33%
Feature Attention
33%
Prediction Task
33%
Domain-specific Languages
33%
Corresponding Features
33%
Diverse Predictions
33%
Regression Task
33%
Computer Science
Deep Neural Network
100%
Pre-Trained Language Models
100%
Image Processing
33%
Text Representation
33%
Decision Tree Model
33%
Classification Task
33%
Language Modeling
33%
Language Processing
33%
Regression Task
33%
Representation Space
33%
Lexical Tokenization
33%
Engineering
Deep Neural Network
100%
Relative Magnitude
33%
Classification Task
33%
Model Tree
33%