Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Investigating Active Learning and Meta-Learning for Iterative Peptide
  Design   Often the development of novel functional peptides is not amenable to high
throughput or purely computational screening methods. Peptides must be
synthesized one at a time in a process that does not generate large amounts of
data. One way this method can be improved is by ensuring that each experiment
provides the best improvement in both peptide properties and predictive
modeling accuracy. Here, we study the effectiveness of active learning,
optimizing experiment order, and meta-learning, transferring knowledge between
contexts, to reduce the number of experiments necessary to build a predictive
model. We present a multi-task benchmark database of peptides designed to
advance these methods for experimental design. Each task is binary
classification of peptides represented as a sequence string. We find neither
active learning method tested to be better than random choice. The
meta-learning method Reptile was found to improve average accuracy across
datasets. Combining meta-learning with active learning offers inconsistent
benefits.

23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph