{"list":"other","total":61,"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":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"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":"2205.11487","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","created":"2022-05-23","cited_by_count":6355},{"id":"2212.09748","title":"Scalable Diffusion Models with Transformers","created":"2022-12-19","cited_by_count":6054},{"id":"2102.09672","title":"Improved Denoising Diffusion Probabilistic Models","created":"2021-02-18","cited_by_count":3761},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"2206.00364","title":"Elucidating the Design Space of Diffusion-Based Generative Models","created":"2022-06-01","cited_by_count":3045},{"id":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"1603.08155","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","created":"2016-03-27","cited_by_count":2694},{"id":"2204.03458","title":"Video Diffusion Models","created":"2022-04-07","cited_by_count":2305},{"id":"1803.08494","title":"Group Normalization","created":"2018-03-22","cited_by_count":1524},{"id":"2304.08818","title":"Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models","created":"2023-04-18","cited_by_count":1476},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114},{"id":"2201.11793","title":"Denoising Diffusion Restoration Models","created":"2022-01-27","cited_by_count":963},{"id":"2002.09405","title":"Learning to Simulate Complex Physics with Graph Networks","created":"2020-02-21","cited_by_count":880},{"id":"2010.03409","title":"Learning Mesh-Based Simulation with Graph Networks","created":"2020-10-07","cited_by_count":868},{"id":"2106.05931","title":"Score-based Generative Modeling in Latent Space","created":"2021-06-10","cited_by_count":636},{"id":"2111.08005","title":"Solving Inverse Problems in Medical Imaging with Score-Based Generative Models","created":"2021-11-15","cited_by_count":521},{"id":"1712.09707","title":"Deep learning for universal linear embeddings of nonlinear dynamics","created":"2017-12-27","cited_by_count":494},{"id":"2102.01010","title":"Machine learning accelerated computational fluid dynamics","created":"2021-01-28","cited_by_count":471},{"id":"2111.15640","title":"Diffusion Autoencoders: Toward a Meaningful and Decodable Representation","created":"2021-11-30","cited_by_count":449},{"id":"2210.07182","title":"PDEBENCH: An Extensive Benchmark for Scientific Machine Learning","created":"2022-10-13","cited_by_count":413},{"id":"2202.03376","title":"Message Passing Neural PDE Solvers","created":"2022-02-07","cited_by_count":386},{"id":"1812.01243","title":"Efficient Attention: Attention with Linear Complexities","created":"2018-12-04","cited_by_count":368},{"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":"2205.11495","title":"Flexible Diffusion Modeling of Long Videos","created":"2022-05-23","cited_by_count":294},{"id":"1602.02644","title":"Generating Images with Perceptual Similarity Metrics based on Deep Networks","created":"2016-02-08","cited_by_count":222},{"id":"1611.02163","title":"Unrolled Generative Adversarial Networks","created":"2016-11-07","cited_by_count":208},{"id":"2007.00016","title":"Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers","created":"2020-06-30","cited_by_count":205},{"id":"1911.08655","title":"Towards Physics-informed Deep Learning for Turbulent Flow Prediction","created":"2019-11-20","cited_by_count":204},{"id":"2111.13606","title":"Conditional Image Generation with Score-Based Diffusion Models","created":"2021-11-26","cited_by_count":197},{"id":"2209.15616","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","created":"2022-09-30","cited_by_count":197},{"id":"1810.08217","title":"Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows","created":"2018-10-18","cited_by_count":150},{"id":"2007.04439","title":"Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction","created":"2020-07-08","cited_by_count":138},{"id":"2206.07696","title":"Diffusion Models for Video Prediction and Infilling","created":"2022-06-15","cited_by_count":135},{"id":"2001.07457","title":"Learning to Control PDEs with Differentiable Physics","created":"2020-01-21","cited_by_count":132},{"id":"1906.05747","title":"Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks","created":"2019-06-13","cited_by_count":129},{"id":"1806.02071","title":"Deep Fluids: A Generative Network for Parameterized Fluid Simulations","created":"2018-06-06","cited_by_count":121},{"id":"2010.03957","title":"Transformers for Modeling Physical Systems","created":"2020-10-04","cited_by_count":106},{"id":"1802.10123","title":"Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow","created":"2018-02-27","cited_by_count":97},{"id":"2112.15275","title":"Learned Coarse Models for Efficient Turbulence Simulation","created":"2021-12-31","cited_by_count":91},{"id":"2201.09113","title":"Predicting Physics in Mesh-reduced Space with Temporal Attention","created":"2022-01-22","cited_by_count":84},{"id":"1802.02674","title":"Efficient collective swimming by harnessing vortices through deep reinforcement learning","created":"2018-02-07","cited_by_count":73},{"id":"2109.05237","title":"Physics-based Deep Learning","created":"2021-09-11","cited_by_count":68},{"id":"2306.01776","title":"From Zero to Turbulence: Generative Modeling for 3D Flow Simulation","created":"2023-05-29","cited_by_count":49},{"id":"1911.09145","title":"DPM: A deep learning PDE augmentation method (with application to large-eddy simulation)","created":"2019-11-20","cited_by_count":41},{"id":"2202.06988","title":"Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss-functions and Optimisation Horizons","created":"2022-02-14","cited_by_count":39},{"id":"2206.07681","title":"Learning to Accelerate Partial Differential Equations via Latent Global Evolution","created":"2022-06-15","cited_by_count":39},{"id":"1807.02901","title":"Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks","created":"2018-07-08","cited_by_count":20},{"id":"2301.10250","title":"Solving Inverse Physics Problems with Score Matching","created":"2023-01-24","cited_by_count":16},{"id":"2002.07863","title":"Learning Similarity Metrics for Numerical Simulations","created":"2020-02-18","cited_by_count":9},{"id":"2210.14675","title":"Comparison of neural closure models for discretised PDEs","created":"2022-10-26","cited_by_count":6}],"next_cursor":null}