Andrea Dittadi | DeepAI

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∙ 04/25/2023

On the Generalization of Learned Structured Representations

Despite tremendous progress over the past decade, deep learning methods ...

0 Andrea Dittadi, et al. ∙

∙ 11/17/2022

Assessing Neural Network Robustness via Adversarial Pivotal Tuning

The ability to assess the robustness of image classifiers to a diverse s...

0 Peter Ebert Christensen, et al. ∙

∙ 10/01/2022

DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability

In representation learning, a common approach is to seek representations...

10 Cian Eastwood, et al. ∙

∙ 07/19/2022

Assaying Out-Of-Distribution Generalization in Transfer Learning

Since out-of-distribution generalization is a generally ill-posed proble...

6 Florian Wenzel, et al. ∙

∙ 06/15/2022

Diffusion Models for Video Prediction and Infilling

To predict and anticipate future outcomes or reason about missing inform...

0 Tobias Höppe, et al. ∙

∙ 04/18/2022

Inductive Biases for Object-Centric Representations of Complex Textures

Understanding which inductive biases could be useful for the unsupervise...

0 Samuele Papa, et al. ∙

∙ 03/17/2022

Image Super-Resolution With Deep Variational Autoencoders

Image super-resolution (SR) techniques are used to generate a high-resol...

0 Darius Chira, et al. ∙

∙ 01/20/2022

Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations

The widespread adoption of electronic health records (EHRs) and subseque...

0 Simon Bing, et al. ∙

∙ 10/07/2021

Boxhead: A Dataset for Learning Hierarchical Representations

Disentanglement is hypothesized to be beneficial towards a number of dow...

2 Yukun Chen, et al. ∙

∙ 07/12/2021

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning

Learning data representations that are useful for various downstream tas...

48 Andrea Dittadi, et al. ∙

∙ 07/01/2021

Generalization and Robustness Implications in Object-Centric Learning

The idea behind object-centric representation learning is that natural s...

1 Andrea Dittadi, et al. ∙

∙ 12/16/2020

Planning From Pixels in Atari With Learned Symbolic Representations

Width-based planning methods have been shown to yield state-of-the-art p...

0 Andrea Dittadi, et al. ∙

∙ 10/27/2020

On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying stru...

24 Andrea Dittadi, et al. ∙

∙ 08/05/2020

Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds

This paper introduces novel results for the score function gradient esti...

6 Valentin Liévin, et al. ∙

∙ 10/10/2019

Semi-Supervised Variational Autoencoder for Survival Prediction

In this paper we propose a semi-supervised variational autoencoder for c...

0 Sveinn Pálsson, et al. ∙

∙ 09/25/2019

LAVAE: Disentangling Location and Appearance

We propose a probabilistic generative model for unsupervised learning of...

7 Andrea Dittadi, et al. ∙

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