{"list":"other","total":51,"rows":[{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"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":"2205.11487","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","created":"2022-05-23","cited_by_count":6355},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"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":"2201.09865","title":"RePaint: Inpainting using Denoising Diffusion Probabilistic Models","created":"2022-01-24","cited_by_count":1430},{"id":"2209.14687","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","created":"2022-09-29","cited_by_count":1416},{"id":"2009.09761","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis","created":"2020-09-21","cited_by_count":1354},{"id":"2205.09991","title":"Planning with Diffusion for Flexible Behavior Synthesis","created":"2022-05-20","cited_by_count":1061},{"id":"2201.11793","title":"Denoising Diffusion Restoration Models","created":"2022-01-27","cited_by_count":963},{"id":"2004.05439","title":"Meta-Learning in Neural Networks: A Survey","created":"2020-04-11","cited_by_count":950},{"id":"2111.08005","title":"Solving Inverse Problems in Medical Imaging with Score-Based Generative Models","created":"2021-11-15","cited_by_count":521},{"id":"2206.00941","title":"Improving Diffusion Models for Inverse Problems using Manifold Constraints","created":"2022-06-02","cited_by_count":508},{"id":"2209.11215","title":"Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions","created":"2022-09-22","cited_by_count":445},{"id":"1707.02038","title":"A Tutorial on Thompson Sampling","created":"2017-07-07","cited_by_count":433},{"id":"2208.04202","title":"Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning","created":"2022-08-08","cited_by_count":373},{"id":"2101.12072","title":"Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting","created":"2021-01-28","cited_by_count":337},{"id":"2201.06503","title":"Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models","created":"2022-01-17","cited_by_count":334},{"id":"2112.05146","title":"Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction","created":"2021-12-09","cited_by_count":307},{"id":"2108.01368","title":"Robust Compressed Sensing MRI with Deep Generative Priors","created":"2021-08-03","cited_by_count":286},{"id":"1301.2609","title":"Learning to Optimize Via Posterior Sampling","created":"2013-01-11","cited_by_count":256},{"id":"2206.09012","title":"Diffusion models as plug-and-play priors","created":"2022-06-17","cited_by_count":231},{"id":"2105.14951","title":"SNIPS: Solving Noisy Inverse Problems Stochastically","created":"2021-05-31","cited_by_count":191},{"id":"2209.12381","title":"Convergence of score-based generative modeling for general data distributions","created":"2022-09-26","cited_by_count":181},{"id":"2211.13449","title":"Fast Sampling of Diffusion Models via Operator Learning","created":"2022-11-24","cited_by_count":175},{"id":"1802.09127","title":"Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling","created":"2018-02-26","cited_by_count":169},{"id":"2210.05475","title":"GENIE: Higher-Order Denoising Diffusion Solvers","created":"2022-10-11","cited_by_count":127},{"id":"2206.07309","title":"Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models","created":"2022-06-15","cited_by_count":83},{"id":"1803.04623","title":"Thompson Sampling for Combinatorial Semi-Bandits","created":"2018-03-13","cited_by_count":78},{"id":"2102.06129","title":"Meta-Thompson Sampling","created":"2021-02-11","cited_by_count":63},{"id":"1911.09724","title":"Information-Theoretic Confidence Bounds for Reinforcement Learning","created":"2019-11-21","cited_by_count":51},{"id":"2206.03992","title":"Neural Diffusion Processes","created":"2022-06-08","cited_by_count":50},{"id":"1805.10531","title":"Unsupervised Learning with Stein's Unbiased Risk Estimator","created":"2018-05-26","cited_by_count":47},{"id":"2005.08531","title":"Meta-learning with Stochastic Linear Bandits","created":"2020-05-18","cited_by_count":46},{"id":"2101.09552","title":"Stochastic Image Denoising by Sampling from the Posterior Distribution","created":"2021-01-23","cited_by_count":45},{"id":"2006.08714","title":"Latent Bandits Revisited","created":"2020-06-15","cited_by_count":44},{"id":"2107.01509","title":"Bayesian decision-making under misspecified priors with applications to meta-learning","created":"2021-07-03","cited_by_count":44},{"id":"1902.10918","title":"Meta Dynamic Pricing: Transfer Learning Across Experiments","created":"2019-02-28","cited_by_count":42},{"id":"2104.04457","title":"Protein sequence design with deep generative models","created":"2021-04-09","cited_by_count":35},{"id":"2107.06196","title":"No Regrets for Learning the Prior in Bandits","created":"2021-07-13","cited_by_count":30},{"id":"2106.03195","title":"Meta-Learning Reliable Priors in the Function Space","created":"2021-06-06","cited_by_count":23},{"id":"2210.15793","title":"Conditioning and Sampling in Variational Diffusion Models for Speech Super-Resolution","created":"2022-10-27","cited_by_count":20},{"id":"2202.01454","title":"Deep Hierarchy in Bandits","created":"2022-02-03","cited_by_count":19},{"id":"2106.05608","title":"Thompson Sampling with a Mixture Prior","created":"2021-06-10","cited_by_count":16},{"id":"2107.05320","title":"Metalearning Linear Bandits by Prior Update","created":"2021-07-12","cited_by_count":16},{"id":"1411.2919","title":"Bounded Regret for Finite-Armed Structured Bandits","created":"2014-11-11","cited_by_count":15},{"id":"2102.07800","title":"Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy","created":"2021-02-15","cited_by_count":8}],"next_cursor":null}