Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Easy High-Dimensional Likelihood-Free Inference   We introduce a framework using Generative Adversarial Networks (GANs) for
likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC)
where we replace the black-box simulator model with an approximator network and
generate a rich set of summary features in a data driven fashion. On benchmark
data sets, our approach improves on others with respect to scalability, ability
to handle high dimensional data and complex probability distributions.

35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
21
The categories of this paper are: adap-org