Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretable Set Functions   We propose learning flexible but interpretable functions that aggregate a
variable-length set of permutation-invariant feature vectors to predict a
label. We use a deep lattice network model so we can architect the model
structure to enhance interpretability, and add monotonicity constraints between
inputs-and-outputs. We then use the proposed set function to automate the
engineering of dense, interpretable features from sparse categorical features,
which we call semantic feature engine. Experiments on real-world data show the
achieved accuracy is similar to deep sets or deep neural networks, and is
easier to debug and understand.

24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat