SciPost Submission Page

Better Latent Spaces for Better Autoencoders

by Barry M. Dillon, Tilman Plehn, Christof Sauer, Peter Sorrenson

Submission summary

As Contributors: Barry Dillon · Tilman Plehn
Arxiv Link: https://arxiv.org/abs/2104.08291v1 (pdf)
Code repository: https://github.com/bmdillon/jet-mixture-vae
Date accepted: 2021-08-12
Date submitted: 2021-05-12 15:19
Submitted by: Dillon, Barry
Submitted to: SciPost Physics
Academic field: Physics
Specialties:
  • High-Energy Physics - Phenomenology
Approaches: Computational, Phenomenological

Abstract

Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other way around. To address this, we derive classifiers from the latent space of (variational) autoencoders, specifically in Gaussian mixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.

Published as SciPost Phys. 11, 061 (2021)



Submission & Refereeing History

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Submission 2104.08291v1 on 12 May 2021

Reports on this Submission

Anonymous Report 1 on 2021-7-26 (Invited Report)

  • Cite as: Anonymous, Report on arXiv:2104.08291v1, delivered 2021-07-25, doi: 10.21468/SciPost.Report.3293

Strengths

1. Well motivated, and excellent description of the motivation.
2. Topical, as illustrated by the other papers that were coordinated with this one.
3. Very clearly described methodology and ML structure.
4. Very nicely formatted, clear plots, a pleasure to read.

Weaknesses

1. Maybe it would have been worthwhile to come up with a concrete signal that might have been missed by existing searches but discovered by a strategy taking advantage of these VAEs.

Report

This is an excellent and very topical paper which will be of great value to the particle physics machine learning community, and I wholeheartedly recommend it for publication. I think there is a lot more to do be done in understanding how the structure of probabilistic latent spaces can be used for unsupervised event classification, and this paper is likely to inspire future work on the topic.

Requested changes

None

  • validity: high
  • significance: good
  • originality: good
  • clarity: top
  • formatting: perfect
  • grammar: perfect

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