Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Probabilistic Logic and Deep Learning for Self-Supervised
  Learning   Deep learning has proven effective for various application tasks, but its
applicability is limited by the reliance on annotated examples. Self-supervised
learning has emerged as a promising direction to alleviate the supervision
bottleneck, but existing work focuses on leveraging co-occurrences in unlabeled
data for task-agnostic representation learning, as exemplified by masked
language model pretraining. In this chapter, we explore task-specific
self-supervision, which leverages domain knowledge to automatically annotate
noisy training examples for end applications, either by introducing labeling
functions for annotating individual instances, or by imposing constraints over
interdependent label decisions. We first present deep probabilistic logic(DPL),
which offers a unifying framework for task-specific self-supervision by
composing probabilistic logic with deep learning. DPL represents unknown labels
as latent variables and incorporates diverse self-supervision using
probabilistic logic to train a deep neural network end-to-end using variational
EM. Next, we present self-supervised self-supervision(S4), which adds to DPL
the capability to learn new self-supervision automatically. Starting from an
initial seed self-supervision, S4 iteratively uses the deep neural network to
propose new self supervision. These are either added directly (a form of
structured self-training) or verified by a human expert (as in feature-based
active learning). Experiments on real-world applications such as biomedical
machine reading and various text classification tasks show that task-specific
self-supervision can effectively leverage domain expertise and often match the
accuracy of supervised methods with a tiny fraction of human effort.

19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas