Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Problem Structure in Deep Declarative Networks: Two Case
  Studies   Deep declarative networks and other recent related works have shown how to
differentiate the solution map of a (continuous) parametrized optimization
problem, opening up the possibility of embedding mathematical optimization
problems into end-to-end learnable models. These differentiability results can
lead to significant memory savings by providing an expression for computing the
derivative without needing to unroll the steps of the forward-pass optimization
procedure during the backward pass. However, the results typically require
inverting a large Hessian matrix, which is computationally expensive when
implemented naively. In this work we study two applications of deep declarative
networks -- robust vector pooling and optimal transport -- and show how problem
structure can be exploited to obtain very efficient backward pass computations
in terms of both time and memory. Our ideas can be used as a guide for
improving the computational performance of other novel deep declarative nodes.

37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex