Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SAINT: Improved Neural Networks for Tabular Data via Row Attention and
  Contrastive Pre-Training   Tabular data underpins numerous high-impact applications of machine learning
from fraud detection to genomics and healthcare. Classical approaches to
solving tabular problems, such as gradient boosting and random forests, are
widely used by practitioners. However, recent deep learning methods have
achieved a degree of performance competitive with popular techniques. We devise
a hybrid deep learning approach to solving tabular data problems. Our method,
SAINT, performs attention over both rows and columns, and it includes an
enhanced embedding method. We also study a new contrastive self-supervised
pre-training method for use when labels are scarce. SAINT consistently improves
performance over previous deep learning methods, and it even outperforms
gradient boosting methods, including XGBoost, CatBoost, and LightGBM, on
average over a variety of benchmark tasks.

23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph