{"list":"other","total":157,"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":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"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":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576},{"id":"2208.01626","title":"Prompt-to-Prompt Image Editing with Cross Attention Control","created":"2022-08-02","cited_by_count":2431},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"2312.10997","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","created":"2023-12-18","cited_by_count":2212},{"id":"1411.1784","title":"Conditional Generative Adversarial Nets","created":"2014-11-06","cited_by_count":2162},{"id":"2211.14730","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","created":"2022-11-27","cited_by_count":2039},{"id":"1506.04214","title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","created":"2015-06-13","cited_by_count":1436},{"id":"2205.13504","title":"Are Transformers Effective for Time Series Forecasting?","created":"2022-05-26","cited_by_count":1424},{"id":"2111.05826","title":"Palette: Image-to-Image Diffusion Models","created":"2021-11-10","cited_by_count":1300},{"id":"2111.09883","title":"Swin Transformer V2: Scaling Up Capacity and Resolution","created":"2021-11-18","cited_by_count":1228},{"id":"1709.04875","title":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","created":"2017-09-14","cited_by_count":1136},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114},{"id":"2401.09417","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","created":"2024-01-17","cited_by_count":1056},{"id":"2209.04747","title":"Diffusion Models in Vision: A Survey","created":"2022-09-10","cited_by_count":1015},{"id":"1802.01933","title":"A Survey Of Methods For Explaining Black Box Models","created":"2018-02-06","cited_by_count":973},{"id":"2310.06625","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","created":"2023-10-10","cited_by_count":931},{"id":"2202.11214","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","created":"2022-02-22","cited_by_count":776},{"id":"2403.07815","title":"Chronos: Learning the Language of Time Series","created":"2024-03-12","cited_by_count":759},{"id":"2310.10688","title":"A decoder-only foundation model for time-series forecasting","created":"2023-10-14","cited_by_count":692},{"id":"1711.10485","title":"AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks","created":"2017-11-28","cited_by_count":664},{"id":"2402.02592","title":"Unified Training of Universal Time Series Forecasting Transformers","created":"2024-02-04","cited_by_count":614},{"id":"2008.07669","title":"HiPPO: Recurrent Memory with Optimal Polynomial Projections","created":"2020-08-17","cited_by_count":605},{"id":"2202.07125","title":"Transformers in Time Series: A Survey","created":"2022-02-15","cited_by_count":497},{"id":"2402.03885","title":"MOMENT: A Family of Open Time-series Foundation Models","created":"2024-02-06","cited_by_count":482},{"id":"2004.02806","title":"A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects","created":"2020-04-01","cited_by_count":397},{"id":"2101.12072","title":"Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting","created":"2021-01-28","cited_by_count":337},{"id":"2301.10343","title":"ClimaX: A foundation model for weather and climate","created":"2023-01-24","cited_by_count":337},{"id":"2402.13178","title":"Benchmarking Retrieval-Augmented Generation for Medicine","created":"2024-02-20","cited_by_count":331},{"id":"2104.00954","title":"Skillful Precipitation Nowcasting using Deep Generative Models of Radar","created":"2021-04-02","cited_by_count":311},{"id":"2302.07944","title":"Effective Data Augmentation With Diffusion Models","created":"2023-02-07","cited_by_count":283},{"id":"2002.00469","title":"WeatherBench: A benchmark dataset for data-driven weather forecasting","created":"2020-02-02","cited_by_count":275},{"id":"2212.12794","title":"GraphCast: Learning skillful medium-range global weather forecasting","created":"2022-12-24","cited_by_count":267},{"id":"2111.13587","title":"Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers","created":"2021-11-24","cited_by_count":248},{"id":"2306.12873","title":"FuXi: A cascade machine learning forecasting system for 15-day global weather forecast","created":"2023-06-22","cited_by_count":241},{"id":"2311.07222","title":"Neural General Circulation Models for Weather and Climate","created":"2023-11-13","cited_by_count":239},{"id":"2308.15560","title":"WeatherBench 2: A benchmark for the next generation of data-driven global weather models","created":"2023-08-29","cited_by_count":236},{"id":"2402.02368","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","created":"2024-02-04","cited_by_count":208},{"id":"2003.01460","title":"Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction","created":"2020-03-03","cited_by_count":195},{"id":"2107.00651","title":"AutoFormer: Searching Transformers for Visual Recognition","created":"2021-07-01","cited_by_count":188},{"id":"1706.03458","title":"Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model","created":"2017-06-12","cited_by_count":182},{"id":"2103.09504","title":"PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning","created":"2021-03-17","cited_by_count":182},{"id":"2312.15796","title":"GenCast: Diffusion-based ensemble forecasting for medium-range weather","created":"2023-12-25","cited_by_count":177},{"id":"2202.07575","title":"Forecasting Global Weather with Graph Neural Networks","created":"2022-02-15","cited_by_count":168},{"id":"1905.03554","title":"1D Convolutional Neural Networks and Applications: A Survey","created":"2019-05-09","cited_by_count":153},{"id":"2207.05833","title":"Earthformer: Exploring Space-Time Transformers for Earth System Forecasting","created":"2022-07-12","cited_by_count":152},{"id":"2003.12140","title":"MetNet: A Neural Weather Model for Precipitation Forecasting","created":"2020-03-24","cited_by_count":131},{"id":"1804.06300","title":"PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning","created":"2018-04-17","cited_by_count":122},{"id":"2312.03876","title":"Scaling transformer neural networks for skillful and reliable medium-range weather forecasting","created":"2023-12-06","cited_by_count":110},{"id":"2306.01984","title":"DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting","created":"2023-06-03","cited_by_count":109},{"id":"2307.10422","title":"PreDiff: Precipitation Nowcasting with Latent Diffusion Models","created":"2023-07-19","cited_by_count":98},{"id":"1711.07970","title":"Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge","created":"2017-11-21","cited_by_count":96},{"id":"2404.10024","title":"ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs","created":"2024-04-15","cited_by_count":82},{"id":"2306.05043","title":"Non-autoregressive Conditional Diffusion Models for Time Series Prediction","created":"2023-06-08","cited_by_count":78},{"id":"2404.03264","title":"Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions","created":"2024-04-04","cited_by_count":77},{"id":"2202.05786","title":"Multi-Modal Knowledge Graph Construction and Application: A Survey","created":"2022-02-11","cited_by_count":74},{"id":"2211.15979","title":"AirFormer: Predicting Nationwide Air Quality in China with Transformers","created":"2022-11-29","cited_by_count":73},{"id":"2404.18886","title":"A Survey on Diffusion Models for Time Series and Spatio-Temporal Data","created":"2024-04-29","cited_by_count":67},{"id":"2312.06734","title":"DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting","created":"2023-12-11","cited_by_count":61},{"id":"2304.12891","title":"Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification","created":"2023-04-25","cited_by_count":56},{"id":"2402.04290","title":"CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling","created":"2024-02-06","cited_by_count":56},{"id":"2002.12898","title":"PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting","created":"2020-02-10","cited_by_count":55},{"id":"2307.01909","title":"ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling","created":"2023-07-04","cited_by_count":47},{"id":"2303.06574","title":"Diffusion Models for Non-autoregressive Text Generation: A Survey","created":"2023-03-12","cited_by_count":44},{"id":"2308.13280","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","created":"2023-08-25","cited_by_count":40},{"id":"2410.18712","title":"Retrieval-Augmented Diffusion Models for Time Series Forecasting","created":"2024-10-24","cited_by_count":39},{"id":"2403.16536","title":"VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting","created":"2024-03-25","cited_by_count":35},{"id":"2312.03014","title":"Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey","created":"2023-12-05","cited_by_count":34},{"id":"2401.13912","title":"A Survey of Deep Learning and Foundation Models for Time Series Forecasting","created":"2024-01-25","cited_by_count":33},{"id":"2405.04285","title":"On the Foundations of Earth and Climate Foundation Models","created":"2024-05-07","cited_by_count":33},{"id":"2405.09308","title":"TimeX++: Learning Time-Series Explanations with Information Bottleneck","created":"2024-05-15","cited_by_count":33},{"id":"2108.12238","title":"Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting","created":"2021-08-27","cited_by_count":32},{"id":"2205.13158","title":"SwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation","created":"2022-05-26","cited_by_count":32},{"id":"2306.03110","title":"SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting","created":"2023-06-05","cited_by_count":32},{"id":"2412.12971","title":"ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting","created":"2024-12-17","cited_by_count":32},{"id":"2409.13598","title":"Prithvi WxC: Foundation Model for Weather and Climate","created":"2024-09-20","cited_by_count":31},{"id":"2410.05431","title":"Continuous Ensemble Weather Forecasting with Diffusion models","created":"2024-10-07","cited_by_count":30},{"id":"2304.08754","title":"W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting","created":"2023-04-18","cited_by_count":29},{"id":"2404.06665","title":"Deep Generative Data Assimilation in Multimodal Setting","created":"2024-04-10","cited_by_count":29},{"id":"2306.14066","title":"SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models","created":"2023-06-24","cited_by_count":28},{"id":"2406.16947","title":"Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales","created":"2024-06-19","cited_by_count":27},{"id":"2412.15687","title":"GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations","created":"2024-12-20","cited_by_count":27}],"next_cursor":"100"}