{"list":"other","total":52,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"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":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2212.09748","title":"Scalable Diffusion Models with Transformers","created":"2022-12-19","cited_by_count":6054},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1607.06450","title":"Layer Normalization","created":"2016-07-21","cited_by_count":5669},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"2206.00364","title":"Elucidating the Design Space of Diffusion-Based Generative Models","created":"2022-06-01","cited_by_count":3045},{"id":"1606.03498","title":"Improved Techniques for Training GANs","created":"2016-06-10","cited_by_count":2970},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1609.05158","title":"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network","created":"2016-09-16","cited_by_count":1204},{"id":"1702.03118","title":"Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning","created":"2017-02-10","cited_by_count":950},{"id":"1711.06077","title":"The Perception-Distortion Tradeoff","created":"2017-11-16","cited_by_count":587},{"id":"2204.13902","title":"Fast Sampling of Diffusion Models with Exponential Integrator","created":"2022-04-29","cited_by_count":532},{"id":"2111.08005","title":"Solving Inverse Problems in Medical Imaging with Score-Based Generative Models","created":"2021-11-15","cited_by_count":521},{"id":"1811.08839","title":"fastMRI: An Open Dataset and Benchmarks for Accelerated MRI","created":"2018-11-21","cited_by_count":470},{"id":"2302.07121","title":"Universal Guidance for Diffusion Models","created":"2023-02-14","cited_by_count":437},{"id":"1704.00447","title":"Learning a Variational Network for Reconstruction of Accelerated MRI Data","created":"2017-04-03","cited_by_count":342},{"id":"2108.01368","title":"Robust Compressed Sensing MRI with Deep Generative Priors","created":"2021-08-03","cited_by_count":286},{"id":"2306.17775","title":"Practical and Asymptotically Exact Conditional Sampling in Diffusion Models","created":"2023-06-30","cited_by_count":232},{"id":"2105.14951","title":"SNIPS: Solving Noisy Inverse Problems Stochastically","created":"2021-05-31","cited_by_count":191},{"id":"2311.16424","title":"Manifold Preserving Guided Diffusion","created":"2023-11-28","cited_by_count":157},{"id":"1408.2156","title":"Statistical guarantees for the EM algorithm: From population to sample-based analysis","created":"2014-08-09","cited_by_count":133},{"id":"2106.07009","title":"Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images","created":"2021-06-13","cited_by_count":113},{"id":"2305.19256","title":"Ambient Diffusion: Learning Clean Distributions from Corrupted Data","created":"2023-05-30","cited_by_count":111},{"id":"2306.10574","title":"Score-based Data Assimilation","created":"2023-06-18","cited_by_count":96},{"id":"2003.05783","title":"Statistical and Topological Properties of Sliced Probability Divergences","created":"2020-03-12","cited_by_count":85},{"id":"2006.08172","title":"Faster Wasserstein Distance Estimation with the Sinkhorn Divergence","created":"2020-06-15","cited_by_count":73},{"id":"2111.04726","title":"Estimating High Order Gradients of the Data Distribution by Denoising","created":"2021-11-08","cited_by_count":64},{"id":"2404.10177","title":"Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data","created":"2024-03-20","cited_by_count":44},{"id":"2305.13128","title":"GSURE-Based Diffusion Model Training with Corrupted Data","created":"2023-05-22","cited_by_count":40},{"id":"1911.09107","title":"OmniFold: A Method to Simultaneously Unfold All Observables","created":"2019-11-20","cited_by_count":31},{"id":"2306.07526","title":"User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems","created":"2023-06-13","cited_by_count":30},{"id":"1707.07113","title":"Adversarial Variational Optimization of Non-Differentiable Simulators","created":"2017-07-22","cited_by_count":22},{"id":"2011.05836","title":"Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference","created":"2020-11-11","cited_by_count":21},{"id":"2211.03812","title":"Posterior samples of source galaxies in strong gravitational lenses with score-based priors","created":"2022-11-07","cited_by_count":20},{"id":"1409.2677","title":"Two Modeling Strategies for Empirical Bayes Estimation","created":"2014-09-09","cited_by_count":16},{"id":"2402.07808","title":"Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation","created":"2024-02-12","cited_by_count":9},{"id":"2206.14820","title":"Strong Lensing Source Reconstruction Using Continuous Neural Fields","created":"2022-06-29","cited_by_count":6},{"id":"2006.09396","title":"Density Deconvolution with Normalizing Flows","created":"2020-06-16","cited_by_count":4},{"id":"2401.00945","title":"A review of Monte Carlo-based versions of the EM algorithm","created":"2024-01-01","cited_by_count":4},{"id":"2311.18012","title":"Bayesian Imaging for Radio Interferometry with Score-Based Priors","created":"2023-11-29","cited_by_count":3}],"next_cursor":null}