Andrea Dittadi

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Stefan BauerHelmholtz | TUM | CIFAREmail verificata su tum.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail verificata su tuebingen.mpg.de
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of DenmarkEmail verificata su bio.ku.dk
Francesco LocatelloAssistant Professor, ISTAEmail verificata su ista.ac.at
Frederik TräubleMax Planck Institute for Intelligent SystemsEmail verificata su tuebingen.mpg.de
Manuel WüthrichHarvard UniversityEmail verificata su seas.harvard.edu
Peter GehlerZalandoEmail verificata su zalando.de
Olivier BachemResearch Scientist, Google BrainEmail verificata su google.com
Anirudh GoyalGoogle DeepMind, Mila, Université de MontréalEmail verificata su umontreal.ca
Niki KilbertusTechnical University of Munich & Helmholtz MunichEmail verificata su tum.de
Arash MehrjouETH Zürich - Max Planck InstituteEmail verificata su tuebingen.mpg.de
Julius von KügelgenETH ZürichEmail verificata su stat.math.ethz.ch
Thomas BroxUniversity of FreiburgEmail verificata su cs.uni-freiburg.de
Florian WenzelCTO/Co-founder at Mirelo AIEmail verificata su mirelo.ai
Chris RussellAssociate Professor, University of OxfordEmail verificata su oii.ox.ac.uk
Max HornApplied Scientist in Causal Representation Learning, Amazon AWS AIEmail verificata su amazon.de
Bernt SchieleProfessor, Max Planck Institute for Informatics, Saarland University, Saarland Informatics CampusEmail verificata su mpi-inf.mpg.de
Tom CashmanMicrosoftEmail verificata su microsoft.com
Jamie ShottonWayveEmail verificata su wayve.ai
Thomas BolanderProfessor, Technical University of DenmarkEmail verificata su dtu.dk

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Andrea Dittadi

Helmholtz AI Munich | TUM

Email verificata su tum.de - Home page

generative models representation learning machine learning deep learning


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
On Disentangled Representations Learned from Correlated Data F Träuble, E Creager, N Kilbertus, F Locatello, A Dittadi, A Goyal, ... ICML 2021, 2021	134	2021
Diffusion Models for Video Prediction and Infilling T Höppe, A Mehrjou, S Bauer, D Nielsen, A Dittadi TMLR, 2022	109	2022
On the Transfer of Disentangled Representations in Realistic Settings A Dittadi, F Träuble, F Locatello, M Wüthrich, V Agrawal, O Winther, ... ICLR 2021, 2021	90	2021
Assaying Out-Of-Distribution Generalization in Transfer Learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022	69	2022
Generalization and Robustness Implications in Object-Centric Learning A Dittadi, S Papa, M De Vita, B Schölkopf, O Winther, F Locatello ICML 2022, 2022	67	2022
Full-Body Motion From a Single Head-Mounted Device: Generating SMPL Poses From Partial Observations A Dittadi, S Dziadzio, D Cosker, B Lundell, TJ Cashman, J Shotton ICCV 2021, 2021	60	2021
Image Super-Resolution With Deep Variational Autoencoders D Chira, I Haralampiev, O Winther, A Dittadi, V Liévin ECCV Workshop on Advances in Image Manipulation, 2022	33	2022
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents A Dittadi, F Träuble, M Wüthrich, F Widmaier, P Gehler, O Winther, ... ICLR 2022, 2022	31*	2022
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability C Eastwood, AL Nicolicioiu, J von Kügelgen, A Kekić, F Träuble, A Dittadi, ... ICLR 2023, 2023	20	2023
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations S Bing, A Dittadi, S Bauer, P Schwab PLOS Digital Health, 2022	14	2022
Planning from Pixels in Atari with Learned Symbolic Representations A Dittadi, FK Drachmann, T Bolander AAAI 2021, 2021	14	2021
Inductive Biases for Object-Centric Representations in the Presence of Complex Textures S Papa, O Winther, A Dittadi UAI Workshop on Causal Representation Learning, 2022	13	2022
Towards Hierarchical Discrete Variational Autoencoders V Liévin, A Dittadi, L Maaløe, O Winther 2nd Symposium on Advances in Approximate Bayesian Inference (AABI 2019), 2019	10	2019
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds V Liévin, A Dittadi, A Christensen, O Winther NeurIPS 2020, 2020	8	2020
Learning to Plan from Raw Data in Grid-based Games A Dittadi, T Bolander, O Winther GCAI 2018, 2018	6	2018
Semi-Supervised Variational Autoencoder for Survival Prediction S Pálsson, S Cerri, A Dittadi*, K Van Leemput MICCAI Brain Lesion Workshop, 2019	5	2019
DiffEnc: Variational Diffusion with a Learned Encoder BMG Nielsen, A Christensen, A Dittadi, O Winther ICLR 2024, 2024	3	2024
LAVAE: Disentangling Location and Appearance A Dittadi, O Winther NeurIPS Workshop on Perception as Generative Reasoning, 2019	3	2019
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models AMK Mamaghan, S Papa, KH Johansson, S Bauer, A Dittadi arXiv preprint arXiv:2407.15589, 2024	2	2024
Boxhead: A Dataset for Learning Hierarchical Representations Y Chen, F Träuble, A Dittadi, S Bauer, B Schölkopf NeurIPS Workshop on Shared Visual Representations in Human and Machine …, 2021	2	2021

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