Search Results - "Dittadi, Andrea"

Diffusion-Based Causal Representation Learning by Karimi Mamaghan, Amir Mohammad, Dittadi, Andrea, Bauer, Stefan, Johansson, Karl Henrik, Quinzan, Francesco

“…Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect…”
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Conditional generation of medical time series for extrapolation to underrepresented populations by Bing, Simon, Dittadi, Andrea, Bauer, Stefan, Schwab, Patrick

“…The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant…”
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Full-Body Motion from a Single Head-Mounted Device: Generating SMPL Poses from Partial Observations by Dittadi, Andrea, Dziadzio, Sebastian, Cosker, Darren, Lundell, Ben, Cashman, Tom, Shotton, Jamie

“…The increased availability and maturity of head-mounted and wearable devices opens up opportunities for remote communication and collaboration. However, the…”
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Conditional generation of medical time series for extrapolation to underrepresented populations by Simon Bing, Andrea Dittadi, Stefan Bauer, Patrick Schwab

“…The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant…”
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Assessing Neural Network Robustness via Adversarial Pivotal Tuning by Christensen, Peter Ebert, Snabjarnarson, Vesteinn, Dittadi, Andrea, Belongie, Serge, Benaim, Sagie

“…The robustness of image classifiers is essential to their deployment in the real world. The ability to assess this resilience to manipulations or deviations…”
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On the Generalization of Learned Structured Representations by Dittadi, Andrea

“…Despite tremendous progress over the past decade, deep learning methods generally fall short of human-level systematic generalization. It has been argued that…”
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Spreading and repetitions in satellite MAC protocols by Biason, Alessandro, Dittadi, Andrea, Zorzi, Michele

“…In satellite networks, the design of MAC schemes which achieve high performance is an important and widely studied problem. Random access is recognized as an…”
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LAVAE: Disentangling Location and Appearance by Dittadi, Andrea, Winther, Ole

“…We propose a probabilistic generative model for unsupervised learning of structured, interpretable, object-based representations of visual scenes. We use…”
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Inductive Biases for Object-Centric Representations in the Presence of Complex Textures by Papa, Samuele, Winther, Ole, Dittadi, Andrea

“…Understanding which inductive biases could be helpful for the unsupervised learning of object-centric representations of natural scenes is challenging. In this…”
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DiffEnc: Variational Diffusion with a Learned Encoder by Nielsen, Beatrix M. G, Christensen, Anders, Dittadi, Andrea, Winther, Ole

“…Diffusion models may be viewed as hierarchical variational autoencoders (VAEs) with two improvements: parameter sharing for the conditional distributions in…”
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Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models by Mamaghan, Amir Mohammad Karimi, Papa, Samuele, Johansson, Karl Henrik, Bauer, Stefan, Dittadi, Andrea

“…Object-centric (OC) representations, which represent the state of a visual scene by modeling it as a composition of objects, have the potential to be used in…”
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Generating Multi-Modal and Multi-Attribute Single-Cell Counts with CFGen by Palma, Alessandro, Richter, Till, Zhang, Hanyi, Lubetzki, Manuel, Tong, Alexander, Dittadi, Andrea, Theis, Fabian

“…Generative modeling of single-cell RNA-seq data has shown invaluable potential in community-driven tasks such as trajectory inference, batch effect removal and…”
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Planning from Pixels in Atari with Learned Symbolic Representations by Dittadi, Andrea, Drachmann, Frederik K, Bolander, Thomas

“…Width-based planning methods have been shown to yield state-of-the-art performance in the Atari 2600 domain using pixel input. One successful approach,…”
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Diffusion Models for Video Prediction and Infilling by Höppe, Tobias, Mehrjou, Arash, Bauer, Stefan, Nielsen, Didrik, Dittadi, Andrea

“…Predicting and anticipating future outcomes or reasoning about missing information in a sequence are critical skills for agents to be able to make intelligent…”
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Image Super-Resolution With Deep Variational Autoencoders by Chira, Darius, Haralampiev, Ilian, Winther, Ole, Dittadi, Andrea, Liévin, Valentin

“…Image super-resolution (SR) techniques are used to generate a high-resolution image from a low-resolution image. Until now, deep generative models such as…”
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Diffusion Based Causal Representation Learning by Mamaghan, Amir Mohammad Karimi, Dittadi, Andrea, Bauer, Stefan, Johansson, Karl Henrik, Quinzan, Francesco

“…Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect…”
Get full text

Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations by Bing, Simon, Dittadi, Andrea, Bauer, Stefan, Schwab, Patrick

“…The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant…”
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Boxhead: A Dataset for Learning Hierarchical Representations by Chen, Yukun, Dittadi, Andrea, Träuble, Frederik, Bauer, Stefan, Schölkopf, Bernhard

“…Disentanglement is hypothesized to be beneficial towards a number of downstream tasks. However, a common assumption in learning disentangled representations is…”
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Assessing Neural Network Robustness via Adversarial Pivotal Tuning by Christensen, Peter Ebert, Snæbjarnarson, Vésteinn, Dittadi, Andrea, Belongie, Serge, Benaim, Sagie

“…The robustness of image classifiers is essential to their deployment in the real world. The ability to assess this resilience to manipulations or deviations…”
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DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability by Eastwood, Cian, Nicolicioiu, Andrei Liviu, von Kügelgen, Julius, Kekić, Armin, Träuble, Frederik, Dittadi, Andrea, Schölkopf, Bernhard

“…In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018)…”
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