19 citations · 22 across the 11 of their papers we have counts for
6 papers · 1 filter
Weakly-Supervised Multimodal Learning on MIMIC-CXR
Andrea Agostini, Daphné Chopard, Yang Meng +5
Multimodal data integration and label scarcity pose significant challenges for machine learning in medical settings. To address these issues, we conduct an in-depth evaluation of t…
Cross-Entropy Is All You Need To Invert the Data Generating Process
Patrik Reizinger, Alice Bizeul, Attila Juhos +4
Supervised learning has become a cornerstone of modern machine learning, yet a comprehensive theory explaining its effectiveness remains elusive. Empirical phenomena, such as neura…
TOAST: Transformer Optimization using Adaptive and Simple Transformations
Irene Cannistraci, Simone Antonelli, Emanuele Palumbo +4
Foundation models achieve state-of-the-art performance across different tasks, but their size and computational demands raise concerns about accessibility and sustainability. Exist…
Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection
Alain Ryser, Thomas M. Sutter, Alexander Marx +1
Anomaly detection focuses on identifying samples that deviate from the norm. Discovering informative representations of normal samples is crucial to detecting anomalies effectively…
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks
Kacper Sokol, Julia E. Vogt
Despite significant progress, evaluation of explainable artificial intelligence remains elusive and challenging. In this paper we propose a fine-grained validation framework that i…
Unity by Diversity: Improved Representation Learning in Multimodal VAEs
Thomas M. Sutter, Yang Meng, Andrea Agostini +5
Variational Autoencoders for multimodal data hold promise for many tasks in data analysis, such as representation learning, conditional generation, and imputation. Current architec…