Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Augmenting Neural Nets with Symbolic Synthesis: Applications to Few-Shot
  Learning   We propose symbolic learning as extensions to standard inductive learning
models such as neural nets as a means to solve few shot learning problems. We
device a class of visual discrimination puzzles that calls for recognizing
objects and object relationships as well learning higher-level concepts from
very few images. We propose a two-phase learning framework that combines models
learned from large data sets using neural nets and symbolic first-order logic
formulas learned from a few shot learning instance. We develop first-order
logic synthesis techniques for discriminating images by using symbolic search
and logic constraint solvers. By augmenting neural nets with them, we develop
and evaluate a tool that can solve few shot visual discrimination puzzles with
interpretable concepts.

19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th