Bump Hunting in Latent Space

03/11/2021
by   Blaž Bortolato, et al.
0

Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and characterise features in measured spectra as caused by the presence of anomalies in a dataset.

READ FULL TEXT
research
07/14/2017

GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures

VAEs (Variational AutoEncoders) have proved to be powerful in the contex...
research
11/29/2022

Identification of Rare Cortical Folding Patterns using Unsupervised Deep Learning

Like fingerprints, cortical folding patterns are unique to each brain ev...
research
11/24/2019

Latent space conditioning for improved classification and anomaly detection

We propose a variational autoencoder to perform improved pre-processing ...
research
09/18/2023

Conditioning Latent-Space Clusters for Real-World Anomaly Classification

Anomalies in the domain of autonomous driving are a major hindrance to t...
research
05/21/2021

Towards Automatic Sizing for PPE with a Point Cloud Based Variational Autoencoder

Sizing and fitting of Personal Protective Equipment (PPE) is a critical ...
research
07/03/2020

Variational Autoencoders for Anomalous Jet Tagging

We present a detailed study on Variational Autoencoders (VAEs) for anoma...
research
11/13/2019

Anomaly Detection in Large Scale Networks with Latent Space Models

We develop a real-time anomaly detection algorithm for directed activity...

Please sign up or login with your details

Forgot password? Click here to reset