Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent
  Structure Learning   Latent structure models are a powerful tool for modeling language data: they
can mitigate the error propagation and annotation bottleneck in pipeline
systems, while simultaneously uncovering linguistic insights about the data.
One challenge with end-to-end training of these models is the argmax operation,
which has null gradient. In this paper, we focus on surrogate gradients, a
popular strategy to deal with this problem. We explore latent structure
learning through the angle of pulling back the downstream learning objective.
In this paradigm, we discover a principled motivation for both the
straight-through estimator (STE) as well as the recently-proposed SPIGOT - a
variant of STE for structured models. Our perspective leads to new algorithms
in the same family. We empirically compare the known and the novel pulled-back
estimators against the popular alternatives, yielding new insight for
practitioners and revealing intriguing failure cases.

32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th