744 citations · 6.5k across the 102 of their papers we have counts for
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LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows
Breno Orzari, Nadezda Chernyavskaya, Raphael Cobe +7
In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data gen…
Knowledge Distillation for Anomaly Detection
Adrian Alan Pol, Ekaterina Govorkova, Sonja Gronroos +5
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model siz…
Triggering Dark Showers with Conditional Dual Auto-Encoders
Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier +2
We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types…
Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC
Rohan Shenoy, Javier Duarte, Christian Herwig +5
The Earth mover's distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss…
Goodness of fit by Neyman-Pearson testing
Gaia Grosso, Marco Letizia, Maurizio Pierini +1
The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family o…
Symbolic Regression on FPGAs for Fast Machine Learning Inference
Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar +7
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitiv…
Progress towards an improved particle flow algorithm at CMS with machine learning
Farouk Mokhtar, Joosep Pata, Javier Duarte +3
The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CER…
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier
Sonja Grönroos, Maurizio Pierini, Nadezda Chernyavskaya
More than a thousand 8" silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Ca…
Towards Optimal Compression: Joint Pruning and Quantization
Ben Zandonati, Glenn Bucagu, Adrian Alan Pol +3
Model compression is instrumental in optimizing deep neural network inference on resource-constrained hardware. The prevailing methods for network compression, namely quantization…
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Julian Schuhmacher, Laura Boggia, Vasilis Belis +7
Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadr…
Quantum anomaly detection in the latent space of proton collision events at the LHC
Vasilis Belis, Kinga Anna Woźniak, Ema Puljak +7
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along w…