Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Designing for the Long Tail of Machine Learning   Recent technical advances has made machine learning (ML) a promising
component to include in end user facing systems. However, user experience (UX)
practitioners face challenges in relating ML to existing user-centered design
processes and how to navigate the possibilities and constraints of this design
space. Drawing on our own experience, we characterize designing within this
space as navigating trade-offs between data gathering, model development and
designing valuable interactions for a given model performance. We suggest that
the theoretical description of how machine learning performance scales with
training data can guide designers in these trade-offs as well as having
implications for prototyping. We exemplify the learning curve's usage by
arguing that a useful pattern is to design an initial system in a bootstrap
phase that aims to exploit the training effect of data collected at increasing
orders of magnitude.

25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an