{"list":"other","total":55,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"2006.09661","title":"Implicit Neural Representations with Periodic Activation Functions","created":"2020-06-17","cited_by_count":2238},{"id":"1910.03771","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","created":"2019-10-09","cited_by_count":2218},{"id":"2006.10739","title":"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains","created":"2020-06-18","cited_by_count":2200},{"id":"2209.14687","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","created":"2022-09-29","cited_by_count":1416},{"id":"2006.09011","title":"Improved Techniques for Training Score-Based Generative Models","created":"2020-06-16","cited_by_count":1043},{"id":"2201.11793","title":"Denoising Diffusion Restoration Models","created":"2022-01-27","cited_by_count":963},{"id":"1604.06174","title":"Training Deep Nets with Sublinear Memory Cost","created":"2016-04-21","cited_by_count":848},{"id":"2103.00112","title":"Transformer in Transformer","created":"2021-02-27","cited_by_count":633},{"id":"1909.01377","title":"Deep Equilibrium Models","created":"2019-09-03","cited_by_count":615},{"id":"1803.04189","title":"Noise2Noise: Learning Image Restoration without Clean Data","created":"2018-03-12","cited_by_count":533},{"id":"2108.01368","title":"Robust Compressed Sensing MRI with Deep Generative Priors","created":"2021-08-03","cited_by_count":286},{"id":"2103.03123","title":"COIN: COmpression with Implicit Neural representations","created":"2021-03-03","cited_by_count":233},{"id":"2004.06688","title":"End-to-End Variational Networks for Accelerated MRI Reconstruction","created":"2020-04-14","cited_by_count":202},{"id":"1905.05406","title":"Plug-and-Play Methods Provably Converge with Properly Trained Denoisers","created":"2019-05-14","cited_by_count":198},{"id":"2310.07204","title":"State of the Art on Diffusion Models for Visual Computing","created":"2023-10-11","cited_by_count":127},{"id":"1810.03982","title":"Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks","created":"2018-10-02","cited_by_count":120},{"id":"2203.17061","title":"Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging","created":"2022-03-31","cited_by_count":96},{"id":"1905.11672","title":"Invertible generative models for inverse problems: mitigating representation error and dataset bias","created":"2019-05-28","cited_by_count":94},{"id":"1910.01684","title":"Time-Dependent Deep Image Prior for Dynamic MRI","created":"2019-10-03","cited_by_count":70},{"id":"2102.06103","title":"Measuring Robustness in Deep Learning Based Compressive Sensing","created":"2021-02-11","cited_by_count":53},{"id":"1805.10531","title":"Unsupervised Learning with Stein's Unbiased Risk Estimator","created":"2018-05-26","cited_by_count":47},{"id":"1910.14634","title":"Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators","created":"2019-10-31","cited_by_count":40},{"id":"2001.01258","title":"The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems","created":"2020-01-05","cited_by_count":40},{"id":"2005.03991","title":"Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation","created":"2020-05-07","cited_by_count":39},{"id":"2008.03410","title":"OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging","created":"2020-08-08","cited_by_count":36},{"id":"2106.05264","title":"NeRF in detail: Learning to sample for view synthesis","created":"2021-06-09","cited_by_count":35},{"id":"1406.2661","title":"Generative Adversarial Networks","created":"2014-06-10","cited_by_count":33},{"id":"2203.08213","title":"HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction","created":"2022-03-15","cited_by_count":30},{"id":"2008.13065","title":"Unsupervised MRI Reconstruction with Generative Adversarial Networks","created":"2020-08-29","cited_by_count":24},{"id":"1906.08754","title":"Learning the Sampling Pattern for MRI","created":"2019-06-20","cited_by_count":21},{"id":"2303.14795","title":"MRI Reconstruction with Side Information using Diffusion Models","created":"2023-03-26","cited_by_count":15},{"id":"2012.06318","title":"Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction","created":"2020-12-09","cited_by_count":14},{"id":"2210.01696","title":"Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDU","created":"2022-10-04","cited_by_count":10},{"id":"2301.10365","title":"Data Consistent Deep Rigid MRI Motion Correction","created":"2023-01-25","cited_by_count":10},{"id":"2312.10271","title":"Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data","created":"2023-12-16","cited_by_count":10},{"id":"2305.19079","title":"Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods","created":"2023-05-30","cited_by_count":8},{"id":"2307.12822","title":"Learning Provably Robust Estimators for Inverse Problems via Jittering","created":"2023-07-24","cited_by_count":8},{"id":"2112.09061","title":"Solving Inverse Problems with NerfGANs","created":"2021-12-16","cited_by_count":7},{"id":"2011.00070","title":"Adversarial Robust Training of Deep Learning MRI Reconstruction Models","created":"2020-10-30","cited_by_count":6},{"id":"2101.08196","title":"Variational manifold learning from incomplete data: application to multislice dynamic MRI","created":"2021-01-20","cited_by_count":6},{"id":"2308.02958","title":"K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets","created":"2023-08-05","cited_by_count":6},{"id":"2303.02254","title":"Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space","created":"2023-03-03","cited_by_count":5},{"id":"2109.11524","title":"End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction","created":"2021-09-23","cited_by_count":4},{"id":"2209.00462","title":"MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation","created":"2022-08-31","cited_by_count":3},{"id":"2209.11030","title":"Stochastic Optimization of 3D Non-Cartesian Sampling Trajectory (SNOPY)","created":"2022-09-22","cited_by_count":3},{"id":"2308.02340","title":"Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation","created":"2023-08-04","cited_by_count":3},{"id":"2306.03284","title":"Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models","created":"2023-06-05","cited_by_count":2},{"id":"2208.13003","title":"Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI","created":"2022-08-27","cited_by_count":1},{"id":"2312.00386","title":"Local monotone operator learning using non-monotone operators: MnM-MOL","created":"2023-12-01","cited_by_count":1}],"next_cursor":null}