activity
20202024
most citedM-VADER: A Model for Diffusion with Multimodal Context

7 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20241 cited

Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic

Zaid Alyafeai, Michael Pieler, Hannah Teufel +8

Large Language Models (LLMs) have shown impressive results in multiple domains of natural language processing (NLP) but are mainly focused on the English language. Recently, more L…

cs.CL20241 cited

Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training

Michael Pieler, Marco Bellagente, Hannah Teufel +9

Recently published work on rephrasing natural text data for pre-training LLMs has shown promising results when combining the original dataset with the synthetically rephrased data.…

cs.CV20227 cited

M-VADER: A Model for Diffusion with Multimodal Context

Samuel Weinbach, Marco Bellagente, Constantin Eichenberg +7

We introduce M-VADER: a diffusion model (DM) for image generation where the output can be specified using arbitrary combinations of images and text. We show how M-VADER enables the…

stat.ML20212 cited

Latent Space Refinement for Deep Generative Models

Ramon Winterhalder, Marco Bellagente, Benjamin Nachman

Deep generative models are becoming widely used across science and industry for a variety of purposes. A common challenge is achieving a precise implicit or explicit representation…

hep-ph2020

Invertible Networks or Partons to Detector and Back Again

Marco Bellagente, Anja Butter, Gregor Kasieczka +5

For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simul…