1 citations · 1 across the 4 of their papers we have counts for
8 papers
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
Arnas Uselis, Andrea Dittadi, Seong Joon Oh
Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although modern models are trained on massiv…
Scalable and Interpretable Representation Alignment with Ordinal Similarity
Diogo Soares, Pankhil Gawade, Andrea Dittadi +1
Evaluating representation similarity is fundamental to representation learning. However, existing metrics suffer from significant limitations: they lack interpretability due to shi…
Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics
Egor Antipov, Alessandro Palma, Lorenzo Consoli +3
Estimating density ratios between pairs of intractable data distributions is a core problem in probabilistic modeling, enabling principled comparisons of sample likelihoods under d…
From Words to Amino Acids: Does the Curse of Depth Persist?
Aleena Siji, Amir Mohammad Karimi Mamaghan, Ferdinand Kapl +9
Protein language models (PLMs) have become widely adopted as general-purpose models, demonstrating strong performance in protein engineering and de novo design. Like large language…
Are Object-Centric Representations Better At Compositional Generalization?
Ferdinand Kapl, Amir Mohammad Karimi Mamaghan, Maximilian Seitzer +4
Compositional generalization, the ability to reason about novel combinations of familiar concepts, is fundamental to human cognition and a critical challenge for machine learning.…
Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction
Vincent Pauline, Tobias Höppe, Tobias Höppe +4
Although diffusion models now occupy a central place in generative modeling, introductory treatments commonly assume Euclidean data and seldom clarify their connection to discrete-…