6 citations · 13 across the 6 of their papers we have counts for
6 papers · 1 filter
From Logits to Hierarchies: Hierarchical Clustering made Simple
Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser +2
The hierarchical structure inherent in many real-world datasets makes the modeling of such hierarchies a crucial objective in both unsupervised and supervised machine learning. Whi…
Identifiability Results for Multimodal Contrastive Learning
Imant Daunhawer, Alice Bizeul, Emanuele Palumbo +2
Contrastive learning is a cornerstone underlying recent progress in multi-view and multimodal learning, e.g., in representation learning with image/caption pairs. While its effecti…
How Robust is Unsupervised Representation Learning to Distribution Shift?
Yuge Shi, Imant Daunhawer, Julia E. Vogt +2
The robustness of machine learning algorithms to distributions shift is primarily discussed in the context of supervised learning (SL). As such, there is a lack of insight on the r…
On the Limitations of Multimodal VAEs
Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong +2
Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data. Yet, despite their advantage of weak supervision, they exhi…
Generalized Multimodal ELBO
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supe…
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena. However, existi…