50 citations · 151 across the 23 of their papers we have counts for
16 papers · 1 filter
Introduction to Stochastic Differential Equations for Generative Machine Learning: A Variational Perspective
Ole Winther, Paul Jeha, Sander Dieleman +3
The use of ordinary and stochastic differential equations has led to substantial progress in generative machine learning with applications to, for example, image, video and biomole…
Logit Distance Bounds Representational Similarity
Beatrix M. G. Nielsen, Emanuele Marconato, Luigi Gresele +2
For a broad family of discriminative models that includes autoregressive language models, identifiability results imply that if two models induce the same conditional distributions…
When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
Beatrix M. G. Nielsen, Emanuele Marconato, Andrea Dittadi +1
When and why representations learned by different deep neural networks are similar is an active research topic. We choose to address these questions from the perspective of identif…
Sparse Shift Autoencoders for Identifying Concepts from Large Language Model Activations
Shruti Joshi, Andrea Dittadi, Sébastien Lachapelle +1
Unsupervised approaches to large language model (LLM) interpretability, such as sparse autoencoders (SAEs), offer a way to decode LLM activations into interpretable and, ideally, c…
Diffusion Based Causal Representation Learning
Amir Mohammad Karimi Mamaghan, Andrea Dittadi, Stefan Bauer +2
Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect estimation and the ident…
DiffEnc: Variational Diffusion with a Learned Encoder
Beatrix M. G. Nielsen, Anders Christensen, Andrea Dittadi +1
Diffusion models may be viewed as hierarchical variational autoencoders (VAEs) with two improvements: parameter sharing for the conditional distributions in the generative process…
On the Generalization of Learned Structured Representations
Andrea Dittadi
Despite tremendous progress over the past decade, deep learning methods generally fall short of human-level systematic generalization. It has been argued that explicitly capturing…
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen +4
In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics fo…
Assaying Out-Of-Distribution Generalization in Transfer Learning
Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler +9
Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance acr…
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
Simon Bing, Andrea Dittadi, Stefan Bauer +1
The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understandi…
Boxhead: A Dataset for Learning Hierarchical Representations
Yukun Chen, Andrea Dittadi, Frederik Träuble +2
Disentanglement is hypothesized to be beneficial towards a number of downstream tasks. However, a common assumption in learning disentangled representations is that the data genera…
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents
Andrea Dittadi, Frederik Träuble, Manuel Wüthrich +7
Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level…
Generalization and Robustness Implications in Object-Centric Learning
Andrea Dittadi, Samuele Papa, Michele De Vita +3
The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed represe…
On the Transfer of Disentangled Representations in Realistic Settings
Andrea Dittadi, Frederik Träuble, Francesco Locatello +5
Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disent…
On Disentangled Representations Learned From Correlated Data
Frederik Träuble, Elliot Creager, Niki Kilbertus +5
The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often…
LAVAE: Disentangling Location and Appearance
Andrea Dittadi, Ole Winther
We propose a probabilistic generative model for unsupervised learning of structured, interpretable, object-based representations of visual scenes. We use amortized variational infe…