{"@attributes":{"version":"2.0"},"channel":{"title":"Jonas Adler","link":"https:\/\/jonasadler.com\/","description":"Recent content on Jonas Adler","generator":"Hugo","language":"en","lastBuildDate":"Tue, 17 Jun 2025 00:00:00 +0000","item":[{"title":"Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities","link":"https:\/\/jonasadler.com\/publications\/gemini-2-5\/","pubDate":"Tue, 17 Jun 2025 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/gemini-2-5\/","description":"The Gemini 2.5 model family, advancing reasoning, multimodality, long context and agentic capabilities."},{"title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","link":"https:\/\/jonasadler.com\/publications\/alphafold3\/","pubDate":"Wed, 08 May 2024 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/alphafold3\/","description":"AlphaFold 3 \u2014 accurate prediction of the structure of complexes including proteins, nucleic acids, ligands and more. (Nature)"},{"title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","link":"https:\/\/jonasadler.com\/publications\/gemini-1-5\/","pubDate":"Fri, 08 Mar 2024 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/gemini-1-5\/","description":"A compute-efficient multimodal model with recall and reasoning over millions of tokens of context."},{"title":"Gemini: A Family of Highly Capable Multimodal Models","link":"https:\/\/jonasadler.com\/publications\/gemini\/","pubDate":"Wed, 06 Dec 2023 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/gemini\/","description":"Google DeepMind&rsquo;s family of natively multimodal models across text, images, audio, video and code."},{"title":"Continuous diffusion for categorical data","link":"https:\/\/jonasadler.com\/publications\/continuous-diffusion\/","pubDate":"Mon, 28 Nov 2022 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/continuous-diffusion\/","description":"A framework (CDCD) for modelling categorical data such as text with continuous diffusion models."},{"title":"Highly accurate protein structure prediction for the human proteome","link":"https:\/\/jonasadler.com\/publications\/alphafold-human-proteome\/","pubDate":"Thu, 22 Jul 2021 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/alphafold-human-proteome\/","description":"Applying AlphaFold at scale to predict structures across the entire human proteome. (Nature)"},{"title":"Highly accurate protein structure prediction with AlphaFold","link":"https:\/\/jonasadler.com\/publications\/alphafold\/","pubDate":"Thu, 15 Jul 2021 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/alphafold\/","description":"AlphaFold \u2014 the deep learning system that solved the protein structure prediction problem. (Nature)"},{"title":"Accelerated Forward-Backward Optimization using Deep Learning","link":"https:\/\/jonasadler.com\/publications\/accelerated_fb\/","pubDate":"Wed, 12 May 2021 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/accelerated_fb\/","description":"Provably convergent convex optimization using Deep Learning."},{"title":"Invited Talk: Deep learning for inverse problems. Where are we, and how far can we go?","link":"https:\/\/jonasadler.com\/talks\/2019gamm\/","pubDate":"Mon, 18 Feb 2019 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2019gamm\/","description":"GAMM \u2014 Vienna, Austria"},{"title":"Invited Talk: Deep Posterior Sampling","link":"https:\/\/jonasadler.com\/talks\/2019basp\/","pubDate":"Mon, 04 Feb 2019 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2019basp\/","description":"BASP Frontiers \u2014 Villars-sur-Ollon, Switzerland"},{"title":"Contributed Talk: Deep Bayesian Inversion","link":"https:\/\/jonasadler.com\/talks\/2019dlip\/","pubDate":"Tue, 22 Jan 2019 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2019dlip\/","description":"Deep Learning and Inverse Problems \u2014 Stockholm, Sweden"},{"title":"Invited Talk: Recent advances in using machine learning for image reconstruction","link":"https:\/\/jonasadler.com\/talks\/2018nyu\/","pubDate":"Wed, 12 Dec 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2018nyu\/","description":"Seminar at New York University \u2014 New York, USA"},{"title":"Oral: Task adapted reconstruction for inverse problems","link":"https:\/\/jonasadler.com\/talks\/2018medneurips\/","pubDate":"Sat, 08 Dec 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2018medneurips\/","description":"Medical imaging meets NeurIPS \u2014 Montreal, Canada"},{"title":"Deep Bayesian Inversion","link":"https:\/\/jonasadler.com\/publications\/deep_bayesian\/","pubDate":"Fri, 16 Nov 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/deep_bayesian\/","description":"We use deep learning to perform bayesian inversion."},{"title":"Invited talk: Deep Learning for Image Reconstruction","link":"https:\/\/jonasadler.com\/talks\/2018unibath\/","pubDate":"Sun, 04 Nov 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2018unibath\/","description":"University of Bath \u2014 Bath, England"},{"title":"Invited talk: Deep Learning for Image Reconstruction","link":"https:\/\/jonasadler.com\/talks\/2018icmsec\/","pubDate":"Fri, 19 Oct 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2018icmsec\/","description":"Chinese Academy of Sciences \u2014 Beijing, China"},{"title":"Task adapted reconstruction for inverse problems","link":"https:\/\/jonasadler.com\/publications\/task_adapted_recon\/","pubDate":"Mon, 27 Aug 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/task_adapted_recon\/","description":"Task adapted reconstruction for inverse problems."},{"title":"Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction","link":"https:\/\/jonasadler.com\/publications\/deep_learning_digital_breast_tomosynthesis\/","pubDate":"Tue, 14 Aug 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/deep_learning_digital_breast_tomosynthesis\/","description":"Extending the Learned Primal-Dual algorithm to digital breast tomosynthesis reconstruction."},{"title":"Data-driven Nonsmooth Optimization","link":"https:\/\/jonasadler.com\/publications\/data_driven_nonsmooth_optimization\/","pubDate":"Thu, 02 Aug 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/data_driven_nonsmooth_optimization\/","description":"We show how learning can be applied to proximal Primal-Dual schemes while still guaranteeing convergence."},{"title":"EDS tomographic reconstruction regularized by total nuclear variation joined with HAADF-STEM tomography","link":"https:\/\/jonasadler.com\/publications\/eds_tomo_recon\/","pubDate":"Wed, 01 Aug 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/eds_tomo_recon\/","description":"Augmenting EDS tomography with HAADF-STEM through total nuclear variation regularization for accurate 3D nanostructure reconstruction."},{"title":"Banach Wasserstein GAN","link":"https:\/\/jonasadler.com\/publications\/bwgan\/","pubDate":"Mon, 18 Jun 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/bwgan\/","description":"Generalizing Wasserstein GAN with gradient penalty to Banach Spaces."},{"title":"Invited talk: Learned Iterative Reconstruction for CT","link":"https:\/\/jonasadler.com\/talks\/2018siam_ct\/","pubDate":"Fri, 08 Jun 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2018siam_ct\/","description":"SIAM conference on imaging science \u2014 Bologna, Italy"},{"title":"Invited talk: Learning to solve inverse problems with ODL","link":"https:\/\/jonasadler.com\/talks\/2018siam_odl\/","pubDate":"Fri, 08 Jun 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2018siam_odl\/","description":"SIAM conference on imaging science \u2014 Bologna, Italy"},{"title":"Invited talk: What Can We Expect? Computable Upper Bounds to Machine Learning in Inverse Problems Using MCMC","link":"https:\/\/jonasadler.com\/talks\/2018hpsc\/","pubDate":"Fri, 23 Mar 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2018hpsc\/","description":"High Performance Scientific Computing \u2014 Hanoi, Vietnam"},{"title":"Contributed talk: Learned iterative reconstruction","link":"https:\/\/jonasadler.com\/talks\/2018ssba\/","pubDate":"Fri, 09 Mar 2018 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2018ssba\/","description":"Swedish Symposium on Image Analysis \u2014 Stockholm, Sweden"},{"title":"Poster: Learning to solve inverse problems using Wasserstein Loss","link":"https:\/\/jonasadler.com\/talks\/2017nips\/","pubDate":"Sat, 09 Dec 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2017nips\/","description":"Neural Information Processing Systems \u2014 Los Angeles, USA"},{"title":"Contributed talk: Learned forward operators: Variational regularization for black-box models","link":"https:\/\/jonasadler.com\/talks\/2017generative_models_parameter\/","pubDate":"Tue, 31 Oct 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2017generative_models_parameter\/","description":"Generative models, parameter learning and sparsity \u2014 Cambridge, UK"},{"title":"Learning to solve inverse problems using Wasserstein loss","link":"https:\/\/jonasadler.com\/publications\/learning_inverse_wasserstein\/","pubDate":"Mon, 30 Oct 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/learning_inverse_wasserstein\/","description":"Compute the image-space Wasserstein loss using Sinkhorn iterations and use it for learning to solve inverse problems."},{"title":"Invited talk: Learned iterative reconstruction schemes, theory and practice","link":"https:\/\/jonasadler.com\/talks\/2017variationalmethodsml\/","pubDate":"Mon, 18 Sep 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2017variationalmethodsml\/","description":"Variational Methods Meet Machine Learning \u2014 Cambridge, UK"},{"title":"Model based learning for accelerated, limited-view 3D photoacoustic tomography","link":"https:\/\/jonasadler.com\/publications\/model-based-learning-photoacoustic\/","pubDate":"Thu, 31 Aug 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/model-based-learning-photoacoustic\/","description":"Using gradient boosting we scale learned iterative reconstruction to a very large scale inverse problem."},{"title":"Learning to reconstruct","link":"https:\/\/jonasadler.com\/posts\/learning_to_reconstruct\/","pubDate":"Sat, 22 Jul 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/posts\/learning_to_reconstruct\/","description":"<p>An introduction to some Machine Learning methods for image reconstruction.<\/p>"},{"title":"Learned Primal-Dual Reconstruction","link":"https:\/\/jonasadler.com\/publications\/learned-primal-dual\/","pubDate":"Wed, 05 Jul 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/learned-primal-dual\/","description":"By learning in both reconstruction and data domain, we can improve image reconstruction."},{"title":"GPUMCI: a flexible platform for x-ray imaging on the GPU","link":"https:\/\/jonasadler.com\/publications\/gpumci\/","pubDate":"Thu, 01 Jun 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/gpumci\/","description":"A flexible GPU-accelerated Monte Carlo platform for simulating x-ray imaging."},{"title":"Invited talk: Using deep learning to reconstruct multi-modal images - A primal dual scheme with examples in PET-MRI","link":"https:\/\/jonasadler.com\/talks\/2017aip\/","pubDate":"Wed, 31 May 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2017aip\/","description":"Applied Inverse Problems \u2014 Hangzhou, China"},{"title":"Solving ill-posed inverse problems using iterative deep neural networks","link":"https:\/\/jonasadler.com\/publications\/learning-to-solve\/","pubDate":"Thu, 13 Apr 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/learning-to-solve\/","description":"A deep learning scheme for solving inverse problems that incorporates knowledge about the data formation process, improving both speed and quality compared to classical methods."},{"title":"A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images","link":"https:\/\/jonasadler.com\/publications\/a_modified_fuzzy_c_means\/","pubDate":"Sun, 01 Jan 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/a_modified_fuzzy_c_means\/","description":"A fuzzy C-means approach to shading correction in craniofacial cone-beam CT images."},{"title":"Spectral CT reconstruction with anti-correlated noise model and joint prior","link":"https:\/\/jonasadler.com\/publications\/spectral_ct_anticorrelated\/","pubDate":"Sun, 01 Jan 2017 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/publications\/spectral_ct_anticorrelated\/","description":"Coupling material-selective basis images in spectral CT via an anti-correlated noise model and a joint prior."},{"title":"Dual GPU configuration in Ubuntu 16.04 and CUDA 8.0","link":"https:\/\/jonasadler.com\/posts\/install_cuda\/","pubDate":"Wed, 28 Dec 2016 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/posts\/install_cuda\/","description":"<p>Instructions for installing and configuring Ubuntu 16.04 LTE on a PC with two GPUs.<\/p>"},{"title":"ODL: A python library for inverse problems","link":"https:\/\/jonasadler.com\/talks\/2015ieeemic\/","pubDate":"Sun, 15 Nov 2015 00:00:00 +0000","guid":"https:\/\/jonasadler.com\/talks\/2015ieeemic\/","description":"IEEE Medical Imaging Conference \u2014 San Diego, USA"}]}}