activity
20202025
most citedDirect Evolutionary Optimization of Variational Autoencoders With Binary Latents

4 citations · 9 across the 6 of their papers we have counts for

collaborators

8 papers

cs.LG2025

ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks

Santiago A. Cadena, Andrea Merlo, Emanuel Laude +8

Stellarators are magnetic confinement devices under active development to deliver steady-state carbon-free fusion energy. Their design involves a high-dimensional, constrained opti…

cs.DC2023

Prototyping a ROOT-based distributed analysis workflow for HL-LHC: the CMS use case

Tommaso Tedeschi, Vincenzo Eduardo Padulano, Daniele Spiga +5

The challenges expected for the next era of the Large Hadron Collider (LHC), both in terms of storage and computing resources, provide LHC experiments with a strong motivation for…

hep-ex2022

Second Analysis Ecosystem Workshop Report

Mohamed Aly, Jackson Burzynski, Bryan Cardwell +66

The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the…

hep-ex2022

HL-LHC Analysis With ROOT

Axel Naumann, Philippe Canal, Enric Tejedor +14

ROOT is high energy physics' software for storing and mining data in a statistically sound way, to publish results with scientific graphics. It is evolving since 25 years, now prov…

hep-ex2022

ROOT for the HL-LHC: data format

Axel Naumann, Philippe Canal, Enric Tejedor +14

This document discusses the state, roadmap, and risks of the foundational components of ROOT with respect to the experiments at the HL-LHC (Run 4 and beyond). As foundational compo…

stat.ML2020★ 3 cited

Evolutionary Variational Optimization of Generative Models

Jakob Drefs, Enrico Guiraud, Jörg Lücke

We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimization and evolutionary algorithms. The combination is realize…

stat.ML2020★ 4 cited

Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents

Enrico Guiraud, Jakob Drefs, Jörg Lücke

Discrete latent variables are considered important for real world data, which has motivated research on Variational Autoencoders (VAEs) with discrete latents. However, standard VAE…

physics.data-an2020★ 2 cited

Software Challenges For HL-LHC Data Analysis

ROOT Team, Kim Albertsson Brann, Guilherme Amadio +21

The high energy physics community is discussing where investment is needed to prepare software for the HL-LHC and its unprecedented challenges. The ROOT project is one of the centr…