@misc{nvidia2025cosmosworldfoundationmodel,
      title={Cosmos World Foundation Model Platform for Physical AI}, 
      author={NVIDIA and : and Niket Agarwal and Arslan Ali and Maciej Bala and Yogesh Balaji and Erik Barker and Tiffany Cai and Prithvijit Chattopadhyay and Yongxin Chen and Yin Cui and Yifan Ding and Daniel Dworakowski and Jiaojiao Fan and Michele Fenzi and Francesco Ferroni and Sanja Fidler and Dieter Fox and Songwei Ge and Yunhao Ge and Jinwei Gu and Siddharth Gururani and Ethan He and Jiahui Huang and Jacob Huffman and Pooya Jannaty and Jingyi Jin and Seung Wook Kim and Gergely Klár and Grace Lam and Shiyi Lan and Laura Leal-Taixe and Anqi Li and Zhaoshuo Li and Chen-Hsuan Lin and Tsung-Yi Lin and Huan Ling and Ming-Yu Liu and Xian Liu and Alice Luo and Qianli Ma and Hanzi Mao and Kaichun Mo and Arsalan Mousavian and Seungjun Nah and Sriharsha Niverty and David Page and Despoina Paschalidou and Zeeshan Patel and Lindsey Pavao and Morteza Ramezanali and Fitsum Reda and Xiaowei Ren and Vasanth Rao Naik Sabavat and Ed Schmerling and Stella Shi and Bartosz Stefaniak and Shitao Tang and Lyne Tchapmi and Przemek Tredak and Wei-Cheng Tseng and Jibin Varghese and Hao Wang and Haoxiang Wang and Heng Wang and Ting-Chun Wang and Fangyin Wei and Xinyue Wei and Jay Zhangjie Wu and Jiashu Xu and Wei Yang and Lin Yen-Chen and Xiaohui Zeng and Yu Zeng and Jing Zhang and Qinsheng Zhang and Yuxuan Zhang and Qingqing Zhao and Artur Zolkowski},
      year={2025},
      eprint={2501.03575},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.03575}, 
}

@article{parkerholder2024genie2,
  title         = {Genie 2: A Large-Scale Foundation World Model},
  author        = {Jack Parker-Holder and Philip Ball and Jake Bruce and Vibhavari Dasagi and Kristian Holsheimer and Christos Kaplanis and Alexandre Moufarek and Guy Scully and Jeremy Shar and Jimmy Shi and Stephen Spencer and Jessica Yung and Michael Dennis and Sultan Kenjeyev and Shangbang Long and Vlad Mnih and Harris Chan and Maxime Gazeau and Bonnie Li and Fabio Pardo and Luyu Wang and Lei Zhang and Frederic Besse and Tim Harley and Anna Mitenkova and Jane Wang and Jeff Clune and Demis Hassabis and Raia Hadsell and Adrian Bolton and Satinder Singh and Tim Rockt{\"a}schel},
  year          = {2024},
  url           = {https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/}
}

@InProceedings{pmlr-v235-bruce24a,
  title = 	 {Genie: Generative Interactive Environments},
  author =       {Bruce, Jake and Dennis, Michael D and Edwards, Ashley and Parker-Holder, Jack and Shi, Yuge and Hughes, Edward and Lai, Matthew and Mavalankar, Aditi and Steigerwald, Richie and Apps, Chris and Aytar, Yusuf and Bechtle, Sarah Maria Elisabeth and Behbahani, Feryal and Chan, Stephanie C.Y. and Heess, Nicolas and Gonzalez, Lucy and Osindero, Simon and Ozair, Sherjil and Reed, Scott and Zhang, Jingwei and Zolna, Konrad and Clune, Jeff and Freitas, Nando De and Singh, Satinder and Rockt\"{a}schel, Tim},
  booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
  pages = 	 {4603--4623},
  year = 	 {2024},
  editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  volume = 	 {235},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {21--27 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v235/main/assets/bruce24a/bruce24a.pdf},
  url = 	 {https://proceedings.mlr.press/v235/bruce24a.html},
  abstract = 	 {We introduce Genie, the first <em>generative interactive environment</em> trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless variety of action-controllable virtual worlds described through text, synthetic images, photographs, and even sketches. At 11B parameters, Genie can be considered a <em>foundation world model</em>. It is comprised of a spatiotemporal video tokenizer, an autoregressive dynamics model, and a simple and scalable latent action model. Genie enables users to act in the generated environments on a frame-by-frame basis <em>despite training without any ground-truth action labels</em> or other domain specific requirements typically found in the world model literature. Further the resulting learned latent action space facilitates training agents to imitate behaviors from unseen videos, opening the path for training generalist agents of the future.}
}


@inproceedings{NEURIPS2024_73c36219,
 author = {Wijeratne, Peter A. and Alexander, Daniel C.},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
 pages = {63316--63341},
 publisher = {Curran Associates, Inc.},
 title = {Unscrambling disease progression at scale: fast inference of event permutations with optimal transport},
 url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/73c362193e3e23188b2a74de7824cc4c-Paper-Conference.pdf},
 volume = {37},
 year = {2024}
}

@Article{Young2018,
author={Young, Alexandra L.
and Marinescu, Razvan V.
and Oxtoby, Neil P.
and Bocchetta, Martina
and Yong, Keir
and Firth, Nicholas C.
and Cash, David M.
and Thomas, David L.
and Dick, Katrina M.
and Cardoso, Jorge
and van Swieten, John
and Borroni, Barbara
and Galimberti, Daniela
and Masellis, Mario
and Tartaglia, Maria Carmela
and Rowe, James B.
and Graff, Caroline
and Tagliavini, Fabrizio
and Frisoni, Giovanni B.
and Laforce, Robert
and Finger, Elizabeth
and de Mendon{\c{c}}a, Alexandre
and Sorbi, Sandro
and Warren, Jason D.
and Crutch, Sebastian
and Fox, Nick C.
and Ourselin, Sebastien
and Schott, Jonathan M.
and Rohrer, Jonathan D.
and Alexander, Daniel C.
and Andersson, Christin
and Archetti, Silvana
and Arighi, Andrea
and Benussi, Luisa
and Binetti, Giuliano
and Black, Sandra
and Cosseddu, Maura
and Fallstr{\"o}m, Marie
and Ferreira, Carlos
and Fenoglio, Chiara
and Freedman, Morris
and Fumagalli, Giorgio G.
and Gazzina, Stefano
and Ghidoni, Roberta
and Grisoli, Marina
and Jelic, Vesna
and Jiskoot, Lize
and Keren, Ron
and Lombardi, Gemma
and Maruta, Carolina
and Meeter, Lieke
and Mead, Simon
and van Minkelen, Rick
and Nacmias, Benedetta
and {\"O}ijerstedt, Linn
and Padovani, Alessandro
and Panman, Jessica
and Pievani, Michela
and Polito, Cristina
and Premi, Enrico
and Prioni, Sara
and Rademakers, Rosa
and Redaelli, Veronica
and Rogaeva, Ekaterina
and Rossi, Giacomina
and Rossor, Martin
and Scarpini, Elio
and Tang-Wai, David
and Thonberg, Hakan
and Tiraboschi, Pietro
and Verdelho, Ana
and Weiner, Michael W.
and Aisen, Paul
and Petersen, Ronald
and Jack, Clifford R.
and Jagust, William
and Trojanowki, John Q.
and Toga, Arthur W.
and Beckett, Laurel
and Green, Robert C.
and Saykin, Andrew J.
and Morris, John
and Shaw, Leslie M.
and Khachaturian, Zaven
and Sorensen, Greg
and Kuller, Lew
and Raichle, Marc
and Paul, Steven
and Davies, Peter
and Fillit, Howard
and Hefti, Franz
and Holtzman, Davie
and Mesulam, M. Marcel
and Potter, William
and Snyder, Peter
and Schwartz, Adam
and Montine, Tom
and Thomas, Ronald G.
and Donohue, Michael
and Walter, Sarah
and Gessert, Devon
and Sather, Tamie
and Jiminez, Gus
and Harvey, Danielle
and Bernstein, Matthew
and Thompson, Paul
and Schuff, Norbert
and Borowski, Bret
and Gunter, Jeff
and Senjem, Matt
and Vemuri, Prashanthi
and Jones, David
and Kantarci, Kejal
and Ward, Chad
and Koeppe, Robert A.
and Foster, Norm
and Reiman, Eric M.
and Chen, Kewei
and Mathis, Chet
and Landau, Susan
and Cairns, Nigel J.
and Householder, Erin
and Taylor-Reinwald, Lisa
and Lee, Virginia
and Korecka, Magdalena
and Figurski, Michal
and Crawford, Karen
and Neu, Scott
and Foroud, Tatiana M.
and Potkin, Steven
and Shen, Li
and Faber, Kelley
and Kim, Sungeun
and Nho, Kwangsik
and Thal, Leon
and Buckholtz, Neil
and Albert, Marylyn
and Frank, Richard
and Hsiao, John
and Kaye, Jeffrey
and Quinn, Joseph
and Lind, Betty
and Carter, Raina
and Dolen, Sara
and Schneider, Lon S.
and Pawluczyk, Sonia
and Beccera, Mauricio
and Teodoro, Liberty
and Spann, Bryan M.
and Brewer, James
and Vanderswag, Helen
and Fleisher, Adam
and Heidebrink, Judith L.
and Lord, Joanne L.
and Mason, Sara S.
and Albers, Colleen S.
and Knopman, David
and Johnson, Kris
and Doody, Rachelle S.
and Villanueva-Meyer, Javier
and Chowdhury, Munir
and Rountree, Susan
and Dang, Mimi
and Stern, Yaakov
and Honig, Lawrence S.
and Bell, Karen L.
and Ances, Beau
and Carroll, Maria
and Leon, Sue
and Mintun, Mark A.
and Schneider, Stacy
and Oliver, Angela
and Marson, Daniel
and Griffith, Randall
and Clark, David
and Geldmacher, David
and Brockington, John
and Roberson, Erik
and Grossman, Hillel
and Mitsis, Effie
and de Toledo-Morrell, Leyla
and Shah, Raj C.
and Duara, Ranjan
and Varon, Daniel
and Greig, Maria T.
and Roberts, Peggy
and Albert, Marilyn
and Onyike, Chiadi
and D'Agostino, Daniel
and Kielb, Stephanie
and Galvin, James E.
and Cerbone, Brittany
and Michel, Christina A.
and Rusinek, Henry
and de Leon, Mony J.
and Glodzik, Lidia
and De Santi, Susan
and Doraiswamy, P. Murali
and Petrella, Jeffrey R.
and Wong, Terence Z.
and Arnold, Steven E.
and Karlawish, Jason H.
and Wolk, David
and Smith, Charles D.
and Jicha, Greg
and Hardy, Peter
and Sinha, Partha
and Oates, Elizabeth
and Conrad, Gary
and Lopez, Oscar L.
and Oakley, MaryAnn
and Simpson, Donna M.
and Porsteinsson, Anton P.
and Goldstein, Bonnie S.
and Martin, Kim
and Makino, Kelly M.
and Ismail, M. Saleem
and Brand, Connie
and Mulnard, Ruth A.
and Thai, Gaby
and Mc-Adams-Ortiz, Catherine
and Womack, Kyle
and Mathews, Dana
and Quiceno, Mary
and Diaz-Arrastia, Ramon
and King, Richard
and Weiner, Myron
and Martin-Cook, Kristen
and DeVous, Michael
and Levey, Allan I.
and Lah, James J.
and Cellar, Janet S.
and Burns, Jeffrey M.
and Anderson, Heather S.
and Swerdlow, Russell H.
and Apostolova, Liana
and Tingus, Kathleen
and Woo, Ellen
and Silverman, Daniel Hs
and Lu, Po H.
and Bartzokis, George
and Graff-Radford, Neill R.
and Parfitt, Francine
and Kendall, Tracy
and Johnson, Heather
and Farlow, Martin R.
and Hake, Ann Marie
and Matthews, Brandy R.
and Herring, Scott
and Hunt, Cynthia
and van Dyck, Christopher H.
and Carson, Richard E.
and MacAvoy, Martha G.
and Chertkow, Howard
and Bergman, Howard
and Hosein, Chris
and Stefanovic, Bojana
and Caldwell, Curtis
and Hsiung, Ging-Yuek Robin
and Feldman, Howard
and Mudge, Benita
and Assaly, Michele
and Kertesz, Andrew
and Rogers, John
and Bernick, Charles
and Munic, Donna
and Kerwin, Diana
and Mesulam, Marek-Marsel
and Lipowski, Kristine
and Wu, Chuang-Kuo
and Johnson, Nancy
and Sadowsky, Carl
and Martinez, Walter
and Villena, Teresa
and Turner, Raymond Scott
and Johnson, Kathleen
and Reynolds, Brigid
and Sperling, Reisa A.
and Johnson, Keith A.
and Marshall, Gad
and Frey, Meghan
and Lane, Barton
and Rosen, Allyson
and Tinklenberg, Jared
and Sabbagh, Marwan N.
and Belden, Christine M.
and Jacobson, Sandra A.
and Sirrel, Sherye A.
and Kowall, Neil
and Killiany, Ronald
and Budson, Andrew E.
and Norbash, Alexander
and Johnson, Patricia Lynn
and Allard, Joanne
and Lerner, Alan
and Ogrocki, Paula
and Hudson, Leon
and Fletcher, Evan
and (GENFI), The Genetic FTD Initiative
and (ADNI), The Alzheimer's Disease Neuroimaging Initiative},
title={Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference},
journal={Nature Communications},
year={2018},
month={Oct},
day={15},
volume={9},
number={1},
pages={4273},
abstract={The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique---Subtype and Stage Inference (SuStaIn)---able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer's disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p{\thinspace}={\thinspace}7.18{\thinspace}{\texttimes}{\thinspace}10−4) or temporal stage (p{\thinspace}={\thinspace}3.96{\thinspace}{\texttimes}{\thinspace}10−5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.},
issn={2041-1723},
doi={10.1038/s41467-018-05892-0},
url={https://doi.org/10.1038/s41467-018-05892-0}
}

@article{FONTEIJN20121880,
title = {An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease},
journal = {NeuroImage},
volume = {60},
number = {3},
pages = {1880-1889},
year = {2012},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2012.01.062},
url = {https://www.sciencedirect.com/science/article/pii/S1053811912000791},
author = {Hubert M. Fonteijn and Marc Modat and Matthew J. Clarkson and Josephine Barnes and Manja Lehmann and Nicola Z. Hobbs and Rachael I. Scahill and Sarah J. Tabrizi and Sebastien Ourselin and Nick C. Fox and Daniel C. Alexander},
keywords = {Disease progression, MRI, Alzheimer's disease, Huntington's disease},
abstract = {Understanding the progression of neurological diseases is vital for accurate and early diagnosis and treatment planning. We introduce a new characterization of disease progression, which describes the disease as a series of events, each comprising a significant change in patient state. We provide novel algorithms to learn the event ordering from heterogeneous measurements over a whole patient cohort and demonstrate using combined imaging and clinical data from familial Alzheimer's and Huntington's disease cohorts. Results provide new detail in the progression pattern of these diseases, while confirming known features, and give unique insight into the variability of progression over the cohort. The key advantage of the new model and algorithms over previous progression models is that they do not require a priori division of the patients into clinical stages. The model and its formulation extend naturally to a wide range of other diseases and developmental processes and accommodate cross-sectional and longitudinal input data.}
}
@article{villemagne2013amyloid,
  title={Amyloid $\beta$ deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study},
  author={Villemagne, Victor L and Burnham, Samantha and Bourgeat, Pierrick and Brown, Belinda and Ellis, Kathryn A and Salvado, Olivier and Szoeke, Cassandra and Macaulay, S Lance and Martins, Ralph and Maruff, Paul and others},
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}
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  year={2013},
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}
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  year={2014},
  organization={Springer}
}
@article{jedynak2012computational,
  title={A computational neurodegenerative disease progression score: method and results with the Alzheimer's disease neuroimaging initiative cohort},
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  publisher={Elsevier}
}
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  title={Estimating long-term multivariate progression from short-term data},
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  pages={S400--S410},
  year={2014},
  publisher={Wiley Online Library}
}
@inproceedings{couronne2021longitudinal,
  title={Longitudinal self-supervision to disentangle inter-patient variability from disease progression},
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  booktitle={International Conference on Medical image computing and computer-assisted intervention},
  pages={231--241},
  year={2021},
  organization={Springer}
}
@inproceedings{sauty2022progression,
  title={Progression models for imaging data with Longitudinal Variational Auto Encoders},
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  pages={3--13},
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  organization={Springer}
}
@article{kim2021longitudinal,
  title={Longitudinal brain MR image modeling using personalized memory for Alzheimer’s disease},
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  year={2023},
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}
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  number={8},
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  year={2022},
  publisher={IEEE}
}
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}
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  volume={35},
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  year={2018},
  publisher={IEEE}
}
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  year={2013}
}
@inproceedings{ho2020denoising,
  title={Denoising diffusion probabilistic models},
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  year={2020}
}
@article{ferreira2022gan,
  title={GAN-based generation of realistic 3D data: A systematic review and taxonomy},
  author={Ferreira, Andr{\'e} and Li, Jianning and Pomykala, Kelsey L and Kleesiek, Jens and Alves, Victor and Egger, Jan},
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  year={2022}
}
@article{singh2021medical,
  title={Medical image generation using generative adversarial networks: A review},
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  year={2021},
  publisher={Springer}
}
@article{sun2022hierarchical,
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  number={8},
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  publisher={IEEE}
}
@inproceedings{uzunova2019multi,
  title={Multi-scale gans for memory-efficient generation of high resolution medical images},
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  pages={112--120},
  year={2019},
  organization={Springer}
}
@article{ferreira2024gan,
  title={GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy},
  author={Ferreira, Andr{\'e} and Li, Jianning and Pomykala, Kelsey L and Kleesiek, Jens and Alves, Victor and Egger, Jan},
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  pages={103100},
  year={2024},
  publisher={Elsevier}
}
@inproceedings{bau2019seeing,
  title={Seeing what a gan cannot generate},
  author={Bau, David and Zhu, Jun-Yan and Wulff, Jonas and Peebles, William and Strobelt, Hendrik and Zhou, Bolei and Torralba, Antonio},
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  pages={4502--4511},
  year={2019}
}
@article{shen2020interfacegan,
  title={Interfacegan: Interpreting the disentangled face representation learned by gans},
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  volume={44},
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@inproceedings{razavi2019generating,
  title={Generating diverse high-fidelity images with vq-vae-2},
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  volume={32},
  year={2019}
}
@inproceedings{esser2021taming,
  title={Taming transformers for high-resolution image synthesis},
  author={Esser, Patrick and Rombach, Robin and Ommer, Bjorn},
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  pages={12873--12883},
  year={2021}
}
@article{liu20243d,
  title={3d brain and heart volume generative models: A survey},
  author={Liu, Yanbin and Dwivedi, Girish and Boussaid, Farid and Bennamoun, Mohammed},
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  volume={56},
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@article{tudosiu2020neuromorphologicaly,
  title={Neuromorphologicaly-preserving volumetric data encoding using VQ-VAE},
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}
@inproceedings{khader2023transformers,
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}
@article{croitoru2023diffusion,
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