{"list":"other","total":105,"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":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"1711.00937","title":"Neural Discrete Representation Learning","created":"2017-11-02","cited_by_count":4712},{"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":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3926},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1606.06650","title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","created":"2016-06-21","cited_by_count":1476},{"id":"1506.04214","title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","created":"2015-06-13","cited_by_count":1436},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"2004.05439","title":"Meta-Learning in Neural Networks: A Survey","created":"2020-04-11","cited_by_count":950},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"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":"2011.06225","title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","created":"2020-11-12","cited_by_count":819},{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680},{"id":"1606.04474","title":"Learning to learn by gradient descent by gradient descent","created":"2016-06-14","cited_by_count":620},{"id":"1902.02476","title":"A Simple Baseline for Bayesian Uncertainty in Deep Learning","created":"2019-02-07","cited_by_count":560},{"id":"2106.04554","title":"A Survey of Transformers","created":"2021-06-08","cited_by_count":401},{"id":"2007.06823","title":"Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users","created":"2020-07-14","cited_by_count":338},{"id":"1503.04069","title":"LSTM: A Search Space Odyssey","created":"2015-03-13","cited_by_count":316},{"id":"2104.00954","title":"Skillful Precipitation Nowcasting using Deep Generative Models of Radar","created":"2021-04-02","cited_by_count":311},{"id":"2002.00469","title":"WeatherBench: A benchmark dataset for data-driven weather forecasting","created":"2020-02-02","cited_by_count":275},{"id":"1708.02709","title":"Recent Trends in Deep Learning Based Natural Language Processing","created":"2017-08-09","cited_by_count":263},{"id":"2003.05385","title":"hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition","created":"2020-03-11","cited_by_count":236},{"id":"1801.06637","title":"Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations","created":"2018-01-20","cited_by_count":235},{"id":"1812.04426","title":"PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network","created":"2018-11-30","cited_by_count":210},{"id":"1911.08655","title":"Towards Physics-informed Deep Learning for Turbulent Flow Prediction","created":"2019-11-20","cited_by_count":204},{"id":"2208.05419","title":"FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators","created":"2022-08-08","cited_by_count":188},{"id":"1609.09869","title":"Structured Inference Networks for Nonlinear State Space Models","created":"2016-09-30","cited_by_count":182},{"id":"2201.07766","title":"Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons","created":"2022-01-19","cited_by_count":170},{"id":"2106.07688","title":"Next Generation Reservoir Computing","created":"2021-06-14","cited_by_count":160},{"id":"1710.04340","title":"Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition","created":"2017-10-12","cited_by_count":158},{"id":"1806.04731","title":"Deep learning to represent sub-grid processes in climate models","created":"2018-06-12","cited_by_count":156},{"id":"1901.05335","title":"A review of domain adaptation without target labels","created":"2019-01-16","cited_by_count":156},{"id":"1802.07486","title":"Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks","created":"2018-02-21","cited_by_count":155},{"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":"1712.01378","title":"Linearly-Recurrent Autoencoder Networks for Learning Dynamics","created":"2017-12-04","cited_by_count":145},{"id":"1711.05501","title":"Sparse identification of nonlinear dynamics for model predictive control in the low-data limit","created":"2017-11-15","cited_by_count":140},{"id":"2110.02085","title":"Enhancing Computational Fluid Dynamics with Machine Learning","created":"2021-10-05","cited_by_count":138},{"id":"2111.10992","title":"Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control","created":"2021-11-22","cited_by_count":137},{"id":"2107.10043","title":"KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics","created":"2021-07-21","cited_by_count":132},{"id":"2002.03061","title":"Incorporating Symmetry into Deep Dynamics Models for Improved Generalization","created":"2020-02-08","cited_by_count":126},{"id":"1806.02071","title":"Deep Fluids: A Generative Network for Parameterized Fluid Simulations","created":"2018-06-06","cited_by_count":121},{"id":"2006.10704","title":"Latent Video Transformer","created":"2020-06-18","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":"2111.08481","title":"PySINDy: A comprehensive Python package for robust sparse system identification","created":"2021-11-12","cited_by_count":89},{"id":"2001.01520","title":"Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model","created":"2020-01-06","cited_by_count":74},{"id":"1906.09686","title":"Quality of Uncertainty Quantification for Bayesian Neural Network Inference","created":"2019-06-24","cited_by_count":59},{"id":"1907.06986","title":"Stochastic gradient Markov chain Monte Carlo","created":"2019-07-16","cited_by_count":56},{"id":"1810.13075","title":"Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles","created":"2018-10-31","cited_by_count":55},{"id":"2010.06772","title":"Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting","created":"2020-10-14","cited_by_count":54},{"id":"1909.04711","title":"Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model","created":"2019-09-10","cited_by_count":45},{"id":"1912.01752","title":"Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability","created":"2019-12-04","cited_by_count":44},{"id":"2010.12605","title":"Using machine learning to correct model error in data assimilation and forecast applications","created":"2020-10-23","cited_by_count":41},{"id":"2009.04318","title":"Combining data assimilation and machine learning to infer unresolved scale parametrisation","created":"2020-09-09","cited_by_count":40},{"id":"1607.04805","title":"Inferring solutions of differential equations using noisy multi-fidelity data","created":"2016-07-16","cited_by_count":38},{"id":"2001.06270","title":"Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization","created":"2020-01-17","cited_by_count":38},{"id":"2107.06658","title":"A Framework for Machine Learning of Model Error in Dynamical Systems","created":"2021-07-14","cited_by_count":38},{"id":"2101.09126","title":"Will Artificial Intelligence supersede Earth System and Climate Models?","created":"2021-01-22","cited_by_count":37},{"id":"1902.11136","title":"Learning Dynamical Systems from Partial Observations","created":"2019-02-26","cited_by_count":36},{"id":"2007.12941","title":"Learning Variational Data Assimilation Models and Solvers","created":"2020-07-25","cited_by_count":36},{"id":"2204.03911","title":"A posteriori learning for quasi-geostrophic turbulence parametrization","created":"2022-04-08","cited_by_count":36},{"id":"2006.04727","title":"Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering","created":"2020-06-08","cited_by_count":34},{"id":"2104.00430","title":"Latent Space Data Assimilation by using Deep Learning","created":"2021-04-01","cited_by_count":30},{"id":"2102.11192","title":"Variational Data Assimilation with a Learned Inverse Observation Operator","created":"2021-02-22","cited_by_count":28},{"id":"2107.07687","title":"Auto-differentiable Ensemble Kalman Filters","created":"2021-07-16","cited_by_count":28},{"id":"2204.03497","title":"Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models","created":"2022-04-07","cited_by_count":26},{"id":"2108.03561","title":"Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations","created":"2021-08-08","cited_by_count":21},{"id":"2209.11371","title":"Ensemble Kalman Methods: A Mean Field Perspective","created":"2022-09-23","cited_by_count":20},{"id":"2008.10532","title":"An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion","created":"2020-08-15","cited_by_count":19},{"id":"1907.02452","title":"Learning Latent Dynamics for Partially-Observed Chaotic Systems","created":"2019-07-04","cited_by_count":18},{"id":"2102.07819","title":"Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components","created":"2021-02-15","cited_by_count":18},{"id":"2004.00707","title":"Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network","created":"2020-03-13","cited_by_count":16},{"id":"1904.10904","title":"Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction","created":"2019-04-24","cited_by_count":15},{"id":"2006.03859","title":"Online learning of both state and dynamics using ensemble Kalman filters","created":"2020-06-06","cited_by_count":15},{"id":"2101.02121","title":"Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation","created":"2021-01-06","cited_by_count":15},{"id":"2011.13466","title":"Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation","created":"2020-11-26","cited_by_count":14},{"id":"2210.13817","title":"Online model error correction with neural networks in the incremental 4D-Var framework","created":"2022-10-25","cited_by_count":14},{"id":"2010.04663","title":"Physical invariance in neural networks for subgrid-scale scalar flux modeling","created":"2020-10-09","cited_by_count":13},{"id":"2107.11114","title":"A comparison of combined data assimilation and machine learning methods for offline and online model error correction","created":"2021-07-23","cited_by_count":13},{"id":"2207.01372","title":"Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies","created":"2022-07-04","cited_by_count":13},{"id":"1903.10335","title":"EM-like Learning Chaotic Dynamics from Noisy and Partial Observations","created":"2019-03-25","cited_by_count":11},{"id":"2105.11828","title":"Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability","created":"2021-05-25","cited_by_count":11},{"id":"2006.13311","title":"70 years of machine learning in geoscience in review","created":"2020-06-16","cited_by_count":10},{"id":"2012.12056","title":"Data Assimilation in the Latent Space of a Neural Network","created":"2020-12-22","cited_by_count":9},{"id":"2106.09658","title":"Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators","created":"2021-06-17","cited_by_count":8},{"id":"2109.08017","title":"Super-resolution data assimilation","created":"2021-09-04","cited_by_count":8},{"id":"2107.11253","title":"State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics","created":"2021-07-23","cited_by_count":7},{"id":"2111.06447","title":"Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks","created":"2021-11-11","cited_by_count":7},{"id":"2111.14844","title":"Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification","created":"2021-11-29","cited_by_count":7},{"id":"2210.17393","title":"Probabilistic Decomposition Transformer for Time Series Forecasting","created":"2022-10-31","cited_by_count":6},{"id":"1712.07003","title":"Bilinear residual Neural Network for the identification and forecasting of dynamical systems","created":"2017-12-19","cited_by_count":5},{"id":"2005.08119","title":"A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks","created":"2020-05-16","cited_by_count":5},{"id":"2202.05750","title":"Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning","created":"2022-02-11","cited_by_count":5},{"id":"2207.05356","title":"An Online Data-Driven Method to Locate Forced Oscillation Sources from Power Plants Based on Sparse Identification of Nonlinear Dynamics (SINDy)","created":"2022-07-12","cited_by_count":5},{"id":"2006.16550","title":"Parameter Estimation of Fire Propagation Models Using Level Set Methods","created":"2020-06-30","cited_by_count":4},{"id":"2101.01568","title":"Adversarially trained LSTMs on reduced order models of urban air pollution simulations","created":"2021-01-05","cited_by_count":4},{"id":"2104.06297","title":"Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations","created":"2021-04-13","cited_by_count":4}],"next_cursor":"100"}