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linh tran she her ph d publications linh tran she her ph d email cv google scholar twitter i am a research scientist at meta working on deep generative models in particular text to image diffusion models before joining meta i was a senior research scientist at autodesk ai lab and interned at google brain and samsung ai before my time in industry i spent several years at imperial college london completing my phd under the supervision of maja pantic i am generally interested in deep probablisitc modeling learning with limited supervision and representation learning i am excited to apply these methods to 2d and 3d computer vision and robotics vision based sim to real transfer further i am also interested in developing interpretable and trustworthy machine learning models my phd research focused on learning interpretable and human controllable representations using deep generative modeling i have been volunteering for several non profit organizations i was fortunate to be one of the organizers of the women in machine learning wiml workshop co located at neurips in 2021 i served as a board member for the non profit organization women in machine learning wiml from april 2022 until july 2023 in collaboration with autodesk foundation and bridges to prosperity i led a multi disciplinary team over six months analyzing geospatial data and developing deep learning models for remote bridge site identification in 2022 outside of work i am mum to a little toddler called milo i enjoy being creative organising events for friends family travelling the world gardening and yoga i am always happy to chat about research and possibilities of collaborations feel free to drop me an email news publications selected publications cauchy schwarz regularized autoencoder l tran m pantic and m p deisenroth journal of machine learning research 2022 arxiv jmlr proceedings code hydra preserving ensemble diversity for model distillation l tran b s veeling k roth j swiatkowski j v dillon j snoek s mandt t salimans s nowozin and r jenatton uncertainty robustness in deep learning udl icml 2020 arxiv long version workshop page workshop paper disentangling geometry and appearance with regularised geometry aware generative adversarial networks l tran j kossaifi y panagakis and m pantic international journal of computer vision pp 1 21 2019 ijcv open access gagan geometry aware generative adversarial networks j kossaifi l tran y panagakis and m pantic proceedings of the ieee conference on computer vision and pattern recognition cvpr 2018 arxiv cvpr proceedings deepcoder semi parametric variational autoencoders for automatic facial action coding d l tran r walecki ognjen oggi rudovic s eleftheriadis b schüller and m pantic denotes equal contribution proceedings of the ieee international conference on computer vision iccv 2017 arxiv iccv proceedings poster conference articles generalizable pose estimation using implicit scene representations v saxena k h maleksha l tran and yotto koga denotes equal supervision international conference on robotics and automation 2023 project arxiv code masktune mitigating spurious correlations by forcing to explore s asgari a khani f khani a gholami l tran ali mahdavi amiri and ghassan hamarneh advances in neural information processing systems neurips 2023 neurips openreview arxiv code simcurl simple contrastive user representation learning from command sequences h chu a h khasahmadi k d d willis f anderson y mao l tran j matejka j vermeulen international conference on machine learning and applications icmla 2023 icmla proceedings arxiv evolving through the looking glass learning improved search spaces with variational autoencoders p j bentley s l lim a gaier and l tran 17th international conference on parallel problem solving from nature ppsn xvii 2022 ppsn proceedings code joinable learning bottom up assembly of parametric cad joints k d d willis p k jayaraman h chu y tian y li d grandi a sanghi l tran j g lambourne a solar lezama and w matusik proceedings of the ieee cvf conference on computer vision and pattern recognition cvpr 2022 arxiv cvpr proceedings poster code dataset how good is the bayes posterior in deep neural networks really f wenzel k roth b s veeling j swiatkowski tran l s mandt j snoek t salimans r jenatton and s nowozin denotes equal contribution 37th international conference on machine learning icml 2020 arxiv icml proceedings github the k tied normal distribution a compact parameterization of gaussian mean field posteriors in bayesian neural networks j swiatkowski k roth b s veeling l tran j v dillon s mandt j snoek t salimans r jenatton and s nowozin 37th international conference on machine learning icml 2020 arxiv icml proceedings journal articles profppidb pairs of physical protein protein interactions predicted for entire proteomes l tran t hamp and b rost plos one 2018 bioarxiv plos one open access project page peer reviewed workshop articles coil constrained optimization in learned latent space p j bentley s l lim a gaier and l tran workshop on enhancing generative machine learning with evolutionary computation egml gecco 2022 arxiv long version workshop page workshop paper code weakly supervised group disentanglement using total correlation l tran s a taghanaki a h khasahmadi and a sanghi workshop on weakly supervised learning weasul iclr 2021 arxiv long version workshop page workshop paper pre prints representation learning for sequential volumetric design tasks m f alam y wang l tran c y cheng and j luo arxiv preprint arxiv 2309 02583 2022 arxiv counterbalancing teacher regularizing batch normalized models for robustness s asgari a gholami f khani k choi l tran r zhang and a khani arxiv preprint arxiv 2207 01548 2022 arxiv other bridges to prosperity geospatial deep learning for remote site identification l tran m nadaf r bushinsky p s alluru d goswami a noriega and j shirley pdf code interdisciplinary machine learning pro bono project for remote bridge site identification with autodesk foundation and bridges to prosperity human controllable and structured deep generative models l tran ph d thesis pdf 2024 linh tran any views expressed here are my own and do not necessarily correspond to my employers past and present