Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ReMix: Calibrated Resampling for Class Imbalance in Deep learning   Class imbalance is a problem of significant importance in applied deep
learning where trained models are exploited for decision support and automated
decisions in critical areas such as health and medicine, transportation, and
finance. The challenge of learning deep models from imbalanced training data
remains high, and the state-of-the-art solutions are typically data dependent
and primarily focused on image data. Real-world imbalanced classification
problems, however, are much more diverse thus necessitating a general solution
that can be applied to tabular, image and text data. In this paper, we propose
ReMix, a training technique that leverages batch resampling, instance mixing
and soft-labels to enable the induction of robust deep models for imbalanced
learning. Our results show that dense nets and CNNs trained with ReMix
generally outperform the alternatives according to the g-mean and are better
calibrated according to the balanced Brier score.

11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin