activity
20152026
most citedCaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds

36 citations · 128 across the 27 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

physics.ins-det2024★ 8 cited

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…

cs.LG2024★ 4 cited

TabDPT: Scaling Tabular Foundation Models on Real Data

Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh +7

Tabular data is one of the most ubiquitous sources of information worldwide, spanning a wide variety of domains. This inherent heterogeneity has slowed the development of Tabular F…

cs.LG2024★ 3 cited

TabPFGen -- Tabular Data Generation with TabPFN

Junwei Ma, Apoorv Dankar, George Stein +2

Advances in deep generative modelling have not translated well to tabular data. We argue that this is caused by a mismatch in structure between popular generative models and discri…

cs.LG2024★ 3 cited

Retrieval & Fine-Tuning for In-Context Tabular Models

Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh +4

Tabular data is a pervasive modality spanning a wide range of domains, and the inherent diversity poses a considerable challenge for deep learning. Recent advancements using transf…

cs.LG2024★ 1 cited

Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh +2

In recent years there has been increased interest in understanding the interplay between deep generative models (DGMs) and the manifold hypothesis. Research in this area focuses on…

cs.LG2024

A Geometric Explanation of the Likelihood OOD Detection Paradox

Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell +3

Likelihood-based deep generative models (DGMs) commonly exhibit a puzzling behaviour: when trained on a relatively complex dataset, they assign higher likelihood values to out-of-d…

cs.LG2024

In-Context Data Distillation with TabPFN

Junwei Ma, Valentin Thomas, Guangwei Yu +1

Foundation models have revolutionized tasks in computer vision and natural language processing. However, in the realm of tabular data, tree-based models like XGBoost continue to do…