{"list":"other","total":103,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1603.02754","title":"XGBoost: A Scalable Tree Boosting System","created":"2016-03-09","cited_by_count":5166},{"id":"2102.12092","title":"Zero-Shot Text-to-Image Generation","created":"2021-02-24","cited_by_count":4288},{"id":"1509.02971","title":"Continuous control with deep reinforcement learning","created":"2015-09-09","cited_by_count":3407},{"id":"1502.03044","title":"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention","created":"2015-02-10","cited_by_count":1423},{"id":"1802.07569","title":"Continual Lifelong Learning with Neural Networks: A Review","created":"2018-02-21","cited_by_count":1330},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"1502.05767","title":"Automatic differentiation in machine learning: a survey","created":"2015-02-20","cited_by_count":855},{"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":"1404.7828","title":"Deep Learning in Neural Networks: An Overview","created":"2014-04-30","cited_by_count":770},{"id":"2009.00236","title":"A Survey of Deep Active Learning","created":"2020-08-30","cited_by_count":554},{"id":"1710.11248","title":"Learning Robust Rewards with Adversarial Inverse Reinforcement Learning","created":"2017-10-30","cited_by_count":490},{"id":"2102.01010","title":"Machine learning accelerated computational fluid dynamics","created":"2021-01-28","cited_by_count":471},{"id":"1904.01681","title":"Augmented Neural ODEs","created":"2019-04-02","cited_by_count":397},{"id":"1905.11075","title":"Machine Learning for Fluid Mechanics","created":"2019-05-27","cited_by_count":385},{"id":"1512.07108","title":"Recent Advances in Convolutional Neural Networks","created":"2015-12-22","cited_by_count":360},{"id":"2003.06097","title":"B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data","created":"2020-03-13","cited_by_count":335},{"id":"1901.06314","title":"Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data","created":"2019-01-18","cited_by_count":262},{"id":"1907.04490","title":"Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning","created":"2019-07-10","cited_by_count":212},{"id":"1802.02871","title":"Online Learning: A Comprehensive Survey","created":"2018-02-08","cited_by_count":205},{"id":"1909.12077","title":"Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control","created":"2019-09-26","cited_by_count":184},{"id":"1909.00590","title":"Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions","created":"2019-09-02","cited_by_count":172},{"id":"1612.08544","title":"Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data","created":"2016-12-27","cited_by_count":171},{"id":"2003.04919","title":"Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems","created":"2020-03-10","cited_by_count":164},{"id":"1905.03554","title":"1D Convolutional Neural Networks and Applications: A Survey","created":"2019-05-09","cited_by_count":153},{"id":"2002.08071","title":"Dissecting Neural ODEs","created":"2020-02-19","cited_by_count":153},{"id":"1909.13334","title":"Symplectic Recurrent Neural Networks","created":"2019-09-29","cited_by_count":150},{"id":"1809.08327","title":"Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems","created":"2018-09-21","cited_by_count":147},{"id":"1909.13789","title":"Hamiltonian Generative Networks","created":"2019-09-30","cited_by_count":137},{"id":"1710.11431","title":"Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling","created":"2017-10-31","cited_by_count":131},{"id":"1909.10145","title":"PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs","created":"2019-09-23","cited_by_count":118},{"id":"1807.10300","title":"Discovering physical concepts with neural networks","created":"2018-07-26","cited_by_count":117},{"id":"2003.02236","title":"Forecasting Sequential Data using Consistent Koopman Autoencoders","created":"2020-03-04","cited_by_count":115},{"id":"1803.08823","title":"A high-bias, low-variance introduction to Machine Learning for physicists","created":"2018-03-23","cited_by_count":108},{"id":"2010.13581","title":"Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints","created":"2020-10-26","cited_by_count":105},{"id":"2010.04456","title":"Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting","created":"2020-10-09","cited_by_count":95},{"id":"1801.01078","title":"Recent Advances in Recurrent Neural Networks","created":"2017-12-29","cited_by_count":89},{"id":"1802.00850","title":"Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification","created":"2018-02-02","cited_by_count":81},{"id":"1910.01719","title":"Digital Twin: Values, Challenges and Enablers","created":"2019-10-03","cited_by_count":81},{"id":"2002.08860","title":"Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning","created":"2020-02-20","cited_by_count":79},{"id":"2102.02770","title":"A Living Review of Machine Learning for Particle Physics","created":"2021-02-02","cited_by_count":78},{"id":"2001.11086","title":"Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles","created":"2020-01-28","cited_by_count":59},{"id":"1407.7502","title":"Understanding Random Forests: From Theory to Practice","created":"2014-07-28","cited_by_count":57},{"id":"1802.04474","title":"Deep Neural Networks Learn Non-Smooth Functions Effectively","created":"2018-02-13","cited_by_count":57},{"id":"1902.00683","title":"Nonlinear System Identification: A User-Oriented Roadmap","created":"2019-02-02","cited_by_count":57},{"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":"2002.10253","title":"Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures","created":"2020-02-18","cited_by_count":52},{"id":"1808.10788","title":"Data-driven discovery of PDEs in complex datasets","created":"2018-08-31","cited_by_count":42},{"id":"1909.08118","title":"Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling","created":"2019-09-17","cited_by_count":40},{"id":"2012.02334","title":"Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data","created":"2020-12-03","cited_by_count":40},{"id":"2107.06658","title":"A Framework for Machine Learning of Model Error in Dynamical Systems","created":"2021-07-14","cited_by_count":38},{"id":"1905.04418","title":"Machine learning in acoustics: theory and applications","created":"2019-05-11","cited_by_count":36},{"id":"1910.09349","title":"Variational Integrator Networks for Physically Structured Embeddings","created":"2019-10-21","cited_by_count":36},{"id":"2102.06794","title":"Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models","created":"2021-02-12","cited_by_count":34},{"id":"2003.04299","title":"Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks","created":"2020-03-09","cited_by_count":33},{"id":"2004.04373","title":"A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications","created":"2020-04-09","cited_by_count":33},{"id":"2005.14617","title":"Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics","created":"2020-05-29","cited_by_count":32},{"id":"1906.06622","title":"Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling","created":"2019-06-15","cited_by_count":28},{"id":"1911.02682","title":"Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling","created":"2019-11-06","cited_by_count":26},{"id":"2104.05314","title":"Machine learning and deep learning","created":"2021-04-12","cited_by_count":26},{"id":"2111.05458","title":"Which priors matter? Benchmarking models for learning latent dynamics","created":"2021-11-09","cited_by_count":26},{"id":"2012.13343","title":"Physics guided machine learning using simplified theories","created":"2020-12-18","cited_by_count":23},{"id":"2012.13869","title":"Neural Closure Models for Dynamical Systems","created":"2020-12-27","cited_by_count":22},{"id":"2001.09292","title":"The role of surrogate models in the development of digital twins of dynamic systems","created":"2020-01-25","cited_by_count":21},{"id":"1608.03990","title":"Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils","created":"2016-08-13","cited_by_count":20},{"id":"1709.00939","title":"DR-RNN: A deep residual recurrent neural network for model reduction","created":"2017-09-04","cited_by_count":20},{"id":"2110.01894","title":"Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models","created":"2021-10-05","cited_by_count":20},{"id":"2007.11362","title":"Time-Reversal Symmetric ODE Network","created":"2020-07-22","cited_by_count":19},{"id":"2102.04458","title":"Detecting Fake News Using Machine Learning : A Systematic Literature Review","created":"2021-02-08","cited_by_count":19},{"id":"1904.00093","title":"A Gaussian process latent force model for joint input-state estimation in linear structural systems","created":"2019-03-29","cited_by_count":18},{"id":"1811.07095","title":"Deep Learning of Turbulent Scalar Mixing","created":"2018-11-17","cited_by_count":17},{"id":"2005.14360","title":"Digital twin, physics-based model, and machine learning applied to damage detection in structures","created":"2020-05-29","cited_by_count":17},{"id":"1812.03511","title":"Physics-informed deep generative models","created":"2018-12-09","cited_by_count":16},{"id":"2108.13054","title":"Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks","created":"2021-08-30","cited_by_count":16},{"id":"2109.11213","title":"Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks","created":"2021-09-23","cited_by_count":16},{"id":"2101.02342","title":"User Response Prediction in Online Advertising","created":"2021-01-07","cited_by_count":15},{"id":"2103.03655","title":"Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces","created":"2021-03-05","cited_by_count":15},{"id":"2105.05622","title":"On risk-based active learning for structural health monitoring","created":"2021-05-12","cited_by_count":15},{"id":"2002.11423","title":"NeuralSens: Sensitivity Analysis of Neural Networks","created":"2020-02-26","cited_by_count":14},{"id":"2102.02617","title":"A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate","created":"2021-02-04","cited_by_count":13},{"id":"1608.06154","title":"Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder","created":"2016-08-22","cited_by_count":11},{"id":"1910.00201","title":"Blending Diverse Physical Priors with Neural Networks","created":"2019-10-01","cited_by_count":11},{"id":"2102.11206","title":"Learning Contact Dynamics using Physically Structured Neural Networks","created":"2021-02-22","cited_by_count":10},{"id":"2011.13497","title":"Physics-Informed Neural State Space Models via Learning and Evolution","created":"2020-11-26","cited_by_count":9},{"id":"2004.04480","title":"Stochastic spectral embedding","created":"2020-04-09","cited_by_count":8},{"id":"2104.12219","title":"Breiman's two cultures: You don't have to choose sides","created":"2021-04-25","cited_by_count":8},{"id":"2010.14236","title":"Scientific intuition inspired by machine learning generated hypotheses","created":"2020-10-27","cited_by_count":7},{"id":"2102.03432","title":"Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes","created":"2021-02-05","cited_by_count":7},{"id":"1709.01073","title":"Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks","created":"2017-09-04","cited_by_count":6},{"id":"2110.07807","title":"Provable Regret Bounds for Deep Online Learning and Control","created":"2021-10-15","cited_by_count":6},{"id":"1709.00845","title":"Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction","created":"2017-09-04","cited_by_count":5},{"id":"1908.10645","title":"Machine-learning techniques for the optimal design of acoustic metamaterials","created":"2019-08-28","cited_by_count":5},{"id":"2001.01596","title":"Deep learning for brake squeal: vibration detection, characterization and prediction","created":"2020-01-02","cited_by_count":5},{"id":"2001.02004","title":"CNN 101: Interactive Visual Learning for Convolutional Neural Networks","created":"2020-01-07","cited_by_count":4},{"id":"2002.03139","title":"Data-driven nonlinear aeroelastic models of morphing wings for control","created":"2020-02-08","cited_by_count":4},{"id":"2103.08349","title":"Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression","created":"2021-03-11","cited_by_count":4},{"id":"2110.03266","title":"Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias","created":"2021-10-07","cited_by_count":4},{"id":"1907.11198","title":"Deep convolutional neural networks for uncertainty propagation in random fields","created":"2019-07-24","cited_by_count":3},{"id":"2102.05497","title":"An Introduction to Gaussian Process Models","created":"2021-02-10","cited_by_count":3},{"id":"2203.01229","title":"On the application of generative adversarial networks for nonlinear modal analysis","created":"2022-03-02","cited_by_count":3}],"next_cursor":"100"}