{"list":"other","total":52,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1312.5602","title":"Playing Atari with Deep Reinforcement Learning","created":"2013-12-19","cited_by_count":3274},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"1711.07971","title":"Non-local Neural Networks","created":"2017-11-21","cited_by_count":2293},{"id":"1704.00028","title":"Improved Training of Wasserstein GANs","created":"2017-03-31","cited_by_count":2235},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1511.06434","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","created":"2015-11-19","cited_by_count":2054},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1610.05492","title":"Federated Learning: Strategies for Improving Communication Efficiency","created":"2016-10-18","cited_by_count":1475},{"id":"1506.04214","title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","created":"2015-06-13","cited_by_count":1436},{"id":"1802.07569","title":"Continual Lifelong Learning with Neural Networks: A Review","created":"2018-02-21","cited_by_count":1330},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"1711.09020","title":"StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation","created":"2017-11-24","cited_by_count":1039},{"id":"1811.08383","title":"TSM: Temporal Shift Module for Efficient Video Understanding","created":"2018-11-20","cited_by_count":856},{"id":"1805.08318","title":"Self-Attention Generative Adversarial Networks","created":"2018-05-21","cited_by_count":809},{"id":"1804.04732","title":"Multimodal Unsupervised Image-to-Image Translation","created":"2018-04-12","cited_by_count":795},{"id":"1701.07875","title":"Wasserstein GAN","created":"2017-01-26","cited_by_count":639},{"id":"1907.04502","title":"DeepXDE: A deep learning library for solving differential equations","created":"2019-07-10","cited_by_count":619},{"id":"1710.07035","title":"Generative Adversarial Networks: An Overview","created":"2017-10-19","cited_by_count":590},{"id":"1606.05908","title":"Tutorial on Variational Autoencoders","created":"2016-06-19","cited_by_count":409},{"id":"1905.11075","title":"Machine Learning for Fluid Mechanics","created":"2019-05-27","cited_by_count":385},{"id":"2001.04385","title":"Universal Differential Equations for Scientific Machine Learning","created":"2020-01-13","cited_by_count":353},{"id":"1806.02920","title":"GAIN: Missing Data Imputation using Generative Adversarial Nets","created":"2018-06-07","cited_by_count":352},{"id":"1812.02391","title":"Meta-Transfer Learning for Few-Shot Learning","created":"2018-12-06","cited_by_count":306},{"id":"1511.02136","title":"Diffusion-Convolutional Neural Networks","created":"2015-11-06","cited_by_count":268},{"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":"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":"1905.08883","title":"Explainable Machine Learning for Scientific Insights and Discoveries","created":"2019-05-21","cited_by_count":166},{"id":"1811.07875","title":"InversionNet: A Real-Time and Accurate Full Waveform Inversion with CNNs and continuous CRFs","created":"2018-10-26","cited_by_count":61},{"id":"1908.05823","title":"A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems","created":"2019-08-16","cited_by_count":59},{"id":"2012.13294","title":"Multi-fidelity Bayesian Neural Networks: Algorithms and Applications","created":"2020-12-19","cited_by_count":59},{"id":"1807.00882","title":"Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media","created":"2018-07-02","cited_by_count":56},{"id":"1708.04975","title":"Training-image based geostatistical inversion using a spatial generative adversarial neural network","created":"2017-08-16","cited_by_count":32},{"id":"1812.09444","title":"Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification","created":"2018-12-22","cited_by_count":26},{"id":"1711.01124","title":"End-to-end Flow Correlation Tracking with Spatial-temporal Attention","created":"2017-11-03","cited_by_count":21},{"id":"1812.06900","title":"Towards a Robust Parameterization for Conditioning Facies Models Using Deep Variational Autoencoders and Ensemble Smoother","created":"2018-12-17","cited_by_count":16},{"id":"2010.03660","title":"Fast Stencil-Code Computation on a Wafer-Scale Processor","created":"2020-10-07","cited_by_count":10},{"id":"1708.01810","title":"Parametrization and generation of geological models with generative adversarial networks","created":"2017-08-05","cited_by_count":9},{"id":"1807.02716","title":"A Deep-Learning-Based Geological Parameterization for History Matching Complex Models","created":"2018-07-07","cited_by_count":7},{"id":"1810.12856","title":"Discovering state-parameter mappings in subsurface models using generative adversarial networks","created":"2018-10-30","cited_by_count":7},{"id":"1810.06118","title":"Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks","created":"2018-10-14","cited_by_count":6},{"id":"1902.01933","title":"Combining Physically-Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn from Mismatch?","created":"2019-01-31","cited_by_count":5},{"id":"2002.05578","title":"Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis","created":"2020-02-13","cited_by_count":5},{"id":"1905.05010","title":"Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns","created":"2019-05-13","cited_by_count":2},{"id":"2006.13304","title":"Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networks","created":"2020-06-16","cited_by_count":2}],"next_cursor":null}