{"list":"other","total":184,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1704.00028","title":"Improved Training of Wasserstein GANs","created":"2017-03-31","cited_by_count":2235},{"id":"2002.02126","title":"LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation","created":"2020-02-06","cited_by_count":1139},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114},{"id":"1708.05031","title":"Neural Collaborative Filtering","created":"2017-08-16","cited_by_count":1111},{"id":"1410.8516","title":"NICE: Non-linear Independent Components Estimation","created":"2014-10-30","cited_by_count":1058},{"id":"1705.09406","title":"Multimodal Machine Learning: A Survey and Taxonomy","created":"2017-05-26","cited_by_count":1013},{"id":"1802.09691","title":"Link Prediction Based on Graph Neural Networks","created":"2018-02-27","cited_by_count":832},{"id":"1601.06759","title":"Pixel Recurrent Neural Networks","created":"2016-01-25","cited_by_count":816},{"id":"1511.06335","title":"Unsupervised Deep Embedding for Clustering Analysis","created":"2015-11-19","cited_by_count":767},{"id":"2307.06435","title":"A Comprehensive Overview of Large Language Models","created":"2023-07-12","cited_by_count":648},{"id":"1606.04934","title":"Improving Variational Inference with Inverse Autoregressive Flow","created":"2016-06-15","cited_by_count":640},{"id":"2107.03502","title":"CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation","created":"2021-07-07","cited_by_count":582},{"id":"1801.07892","title":"Generative Image Inpainting with Contextual Attention","created":"2018-01-24","cited_by_count":580},{"id":"1911.06455","title":"Graph Transformer Networks","created":"2019-11-06","cited_by_count":523},{"id":"1509.00519","title":"Importance Weighted Autoencoders","created":"2015-09-01","cited_by_count":456},{"id":"1701.05517","title":"PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications","created":"2017-01-19","cited_by_count":417},{"id":"1806.02920","title":"GAIN: Missing Data Imputation using Generative Adversarial Nets","created":"2018-06-07","cited_by_count":352},{"id":"1709.02540","title":"The Expressive Power of Neural Networks: A View from the Width","created":"2017-09-08","cited_by_count":320},{"id":"1706.00292","title":"Learning Generative Models with Sinkhorn Divergences","created":"2017-06-01","cited_by_count":314},{"id":"1805.10572","title":"BRITS: Bidirectional Recurrent Imputation for Time Series","created":"2018-05-27","cited_by_count":290},{"id":"1901.09590","title":"TuckER: Tensor Factorization for Knowledge Graph Completion","created":"2019-01-28","cited_by_count":290},{"id":"2106.06935","title":"Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction","created":"2021-06-13","cited_by_count":275},{"id":"2301.04856","title":"Multimodal Deep Learning","created":"2023-01-12","cited_by_count":258},{"id":"1907.04155","title":"GP-VAE: Deep Probabilistic Time Series Imputation","created":"2019-07-09","cited_by_count":172},{"id":"2007.02500","title":"Deep Learning for Anomaly Detection: A Review","created":"2020-07-06","cited_by_count":159},{"id":"1806.07297","title":"Canonical Tensor Decomposition for Knowledge Base Completion","created":"2018-06-19","cited_by_count":150},{"id":"1807.03653","title":"Handling Incomplete Heterogeneous Data using VAEs","created":"2018-07-10","cited_by_count":149},{"id":"1706.03446","title":"Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis","created":"2017-06-12","cited_by_count":148},{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145},{"id":"2008.05756","title":"Metrics for Multi-Class Classification: an Overview","created":"2020-08-13","cited_by_count":143},{"id":"2010.16418","title":"Handling Missing Data with Graph Representation Learning","created":"2020-10-30","cited_by_count":133},{"id":"1606.05535","title":"Tensor Ring Decomposition","created":"2016-06-17","cited_by_count":127},{"id":"1905.02884","title":"Deep Flow-Guided Video Inpainting","created":"2019-05-08","cited_by_count":116},{"id":"2002.03860","title":"Missing Data Imputation using Optimal Transport","created":"2020-02-10","cited_by_count":100},{"id":"1902.04864","title":"A Survey on Session-based Recommender Systems","created":"2019-02-13","cited_by_count":97},{"id":"1709.02508","title":"Deep Subspace Clustering Networks","created":"2017-09-08","cited_by_count":94},{"id":"2203.02167","title":"SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models","created":"2022-03-04","cited_by_count":91},{"id":"1909.06399","title":"Image inpainting: A review","created":"2019-09-13","cited_by_count":89},{"id":"2010.10046","title":"Line Graph Neural Networks for Link Prediction","created":"2020-10-20","cited_by_count":89},{"id":"1904.12058","title":"Inductive Matrix Completion Based on Graph Neural Networks","created":"2019-04-26","cited_by_count":86},{"id":"2206.07769","title":"HyperImpute: Generalized Iterative Imputation with Automatic Model Selection","created":"2022-06-15","cited_by_count":83},{"id":"2206.04216","title":"Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction","created":"2022-06-09","cited_by_count":78},{"id":"2210.04142","title":"Deep Clustering: A Comprehensive Survey","created":"2022-10-09","cited_by_count":78},{"id":"2210.17128","title":"Diffusion models for missing value imputation in tabular data","created":"2022-10-31","cited_by_count":77},{"id":"1902.09599","title":"MisGAN: Learning from Incomplete Data with Generative Adversarial Networks","created":"2019-02-25","cited_by_count":75},{"id":"1502.04689","title":"Exact tensor completion using t-SVD","created":"2015-02-16","cited_by_count":70},{"id":"2306.10453","title":"Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking","created":"2023-06-18","cited_by_count":69},{"id":"1401.6497","title":"Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination","created":"2014-01-25","cited_by_count":65},{"id":"1505.03654","title":"Neural Network with Unbounded Activation Functions is Universal Approximator","created":"2015-05-14","cited_by_count":63},{"id":"2309.09968","title":"Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees","created":"2023-09-18","cited_by_count":62},{"id":"1606.01500","title":"Efficient tensor completion for color image and video recovery: Low-rank tensor train","created":"2016-06-05","cited_by_count":61},{"id":"2201.08236","title":"Temporal Knowledge Graph Completion: A Survey","created":"2022-01-16","cited_by_count":61},{"id":"2402.04059","title":"Deep Learning for Multivariate Time Series Imputation: A Survey","created":"2024-02-06","cited_by_count":57},{"id":"2309.00608","title":"Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair","created":"2023-09-01","cited_by_count":55},{"id":"1905.01907","title":"Missing Data Imputation with Adversarially-trained Graph Convolutional Networks","created":"2019-05-06","cited_by_count":54},{"id":"1911.04206","title":"Practical Federated Gradient Boosting Decision Trees","created":"2019-11-11","cited_by_count":54},{"id":"2310.06671","title":"Making Large Language Models Perform Better in Knowledge Graph Completion","created":"2023-10-10","cited_by_count":48},{"id":"1301.7363","title":"Empirical Analysis of Predictive Algorithms for Collaborative Filtering","created":"2013-01-30","cited_by_count":47},{"id":"1807.04010","title":"Causal Discovery in the Presence of Missing Data","created":"2018-07-11","cited_by_count":47},{"id":"1905.01395","title":"On the Difficulty of Evaluating Baselines: A Study on Recommender Systems","created":"2019-05-04","cited_by_count":46},{"id":"1707.08184","title":"Efficient Low Rank Tensor Ring Completion","created":"2017-07-23","cited_by_count":45},{"id":"2401.03395","title":"Deep Learning-based Image and Video Inpainting: A Survey","created":"2024-01-07","cited_by_count":44},{"id":"2308.13916","title":"Exploring Large Language Models for Knowledge Graph Completion","created":"2023-08-26","cited_by_count":43},{"id":"1809.02288","title":"Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion","created":"2018-09-07","cited_by_count":42},{"id":"2302.02155","title":"Guaranteed Tensor Recovery Fused Low-rankness and Smoothness","created":"2023-02-04","cited_by_count":41},{"id":"2307.00467","title":"MissDiff: Training Diffusion Models on Tabular Data with Missing Values","created":"2023-07-02","cited_by_count":39},{"id":"2402.16197","title":"Language Models for Code Completion: A Practical Evaluation","created":"2024-02-25","cited_by_count":39},{"id":"1307.5870","title":"Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery","created":"2013-07-22","cited_by_count":37},{"id":"1805.07405","title":"Processing of missing data by neural networks","created":"2018-05-18","cited_by_count":37},{"id":"1909.06982","title":"Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion","created":"2019-09-16","cited_by_count":37},{"id":"2003.12628","title":"MCFlow: Monte Carlo Flow Models for Data Imputation","created":"2020-03-27","cited_by_count":37},{"id":"2309.13793","title":"ReMasker: Imputing Tabular Data with Masked Autoencoding","created":"2023-09-25","cited_by_count":37},{"id":"2402.02389","title":"KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion","created":"2024-02-04","cited_by_count":36},{"id":"2403.07311","title":"Knowledge Graph Large Language Model (KG-LLM) for Link Prediction","created":"2024-03-12","cited_by_count":36},{"id":"1911.04436","title":"Nonconvex Low-Rank Tensor Completion from Noisy Data","created":"2019-11-11","cited_by_count":35},{"id":"2011.11347","title":"Time Series Data Imputation: A Survey on Deep Learning Approaches","created":"2020-11-23","cited_by_count":34},{"id":"2308.16361","title":"Large Language Models as Data Preprocessors","created":"2023-08-30","cited_by_count":34},{"id":"1708.06724","title":"VIGAN: Missing View Imputation with Generative Adversarial Networks","created":"2017-08-22","cited_by_count":33},{"id":"2406.12747","title":"TSI-Bench: Benchmarking Time Series Imputation","created":"2024-06-18","cited_by_count":33},{"id":"1312.0925","title":"Understanding Alternating Minimization for Matrix Completion","created":"2013-12-03","cited_by_count":32},{"id":"1405.1773","title":"On Tensor Completion via Nuclear Norm Minimization","created":"2014-05-07","cited_by_count":32},{"id":"1406.2784","title":"Provable Tensor Factorization with Missing Data","created":"2014-06-11","cited_by_count":31},{"id":"2208.08040","title":"A Survey on Incomplete Multi-view Clustering","created":"2022-08-17","cited_by_count":31},{"id":"2302.10363","title":"Transformed Distribution Matching for Missing Value Imputation","created":"2023-02-20","cited_by_count":31},{"id":"1806.00811","title":"Causal Inference with Noisy and Missing Covariates via Matrix Factorization","created":"2018-06-03","cited_by_count":30},{"id":"2006.05301","title":"VAEs in the Presence of Missing Data","created":"2020-06-09","cited_by_count":30},{"id":"2302.10902","title":"Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking","created":"2023-02-10","cited_by_count":30},{"id":"1512.01003","title":"Weighted Schatten $p$-Norm Minimization for Image Denoising and Background Subtraction","created":"2015-12-03","cited_by_count":29},{"id":"1802.08784","title":"Facial Expression Analysis under Partial Occlusion: A Survey","created":"2018-02-24","cited_by_count":28},{"id":"2104.14526","title":"Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements","created":"2021-04-29","cited_by_count":28}],"next_cursor":"100"}