{"list":"other","total":71,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"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":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"2005.12872","title":"End-to-End Object Detection with Transformers","created":"2020-05-26","cited_by_count":7414},{"id":"2108.07258","title":"On the Opportunities and Risks of Foundation Models","created":"2021-08-16","cited_by_count":4033},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1812.06127","title":"Federated Optimization in Heterogeneous Networks","created":"2018-12-14","cited_by_count":2987},{"id":"2004.11362","title":"Supervised Contrastive Learning","created":"2020-04-23","cited_by_count":2829},{"id":"1306.5151","title":"Fine-Grained Visual Classification of Aircraft","created":"2013-06-21","cited_by_count":1726},{"id":"1504.00325","title":"Microsoft COCO Captions: Data Collection and Evaluation Server","created":"2015-04-01","cited_by_count":1462},{"id":"1812.01754","title":"Moment Matching for Multi-Source Domain Adaptation","created":"2018-12-04","cited_by_count":1331},{"id":"1808.06670","title":"Learning deep representations by mutual information estimation and maximization","created":"2018-08-20","cited_by_count":1273},{"id":"1804.06872","title":"Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels","created":"2018-04-18","cited_by_count":1059},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"2012.00364","title":"Pre-Trained Image Processing Transformer","created":"2020-12-01","cited_by_count":893},{"id":"2103.16257","title":"Model-Contrastive Federated Learning","created":"2021-03-30","cited_by_count":766},{"id":"2006.08218","title":"Self-supervised Learning: Generative or Contrastive","created":"2020-06-15","cited_by_count":694},{"id":"2006.07242","title":"Ensemble Distillation for Robust Model Fusion in Federated Learning","created":"2020-06-12","cited_by_count":682},{"id":"2012.04221","title":"Ditto: Fair and Robust Federated Learning Through Personalization","created":"2020-12-08","cited_by_count":672},{"id":"2006.08848","title":"Personalized Federated Learning with Moreau Envelopes","created":"2020-06-16","cited_by_count":618},{"id":"2102.07623","title":"FedBN: Federated Learning on Non-IID Features via Local Batch Normalization","created":"2021-02-15","cited_by_count":571},{"id":"2002.06440","title":"Federated Learning with Matched Averaging","created":"2020-02-15","cited_by_count":564},{"id":"2102.07078","title":"Exploiting Shared Representations for Personalized Federated Learning","created":"2021-02-14","cited_by_count":541},{"id":"2006.04088","title":"An Efficient Framework for Clustered Federated Learning","created":"2020-06-07","cited_by_count":511},{"id":"1905.10497","title":"Fair Resource Allocation in Federated Learning","created":"2019-05-25","cited_by_count":394},{"id":"2002.10619","title":"Three Approaches for Personalization with Applications to Federated Learning","created":"2020-02-25","cited_by_count":325},{"id":"2106.07139","title":"Pre-Trained Models: Past, Present and Future","created":"2021-06-14","cited_by_count":319},{"id":"1903.03096","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","created":"2019-03-07","cited_by_count":315},{"id":"2002.07948","title":"Personalized Federated Learning: A Meta-Learning Approach","created":"2020-02-19","cited_by_count":312},{"id":"2007.03304","title":"Learning to Generate Novel Domains for Domain Generalization","created":"2020-07-07","cited_by_count":296},{"id":"1909.12488","title":"Improving Federated Learning Personalization via Model Agnostic Meta Learning","created":"2019-09-27","cited_by_count":269},{"id":"2106.05001","title":"No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data","created":"2021-06-09","cited_by_count":241},{"id":"1904.03436","title":"Unsupervised Embedding Learning via Invariant and Spreading Instance Feature","created":"2019-04-06","cited_by_count":232},{"id":"2103.04628","title":"Personalized Federated Learning using Hypernetworks","created":"2021-03-08","cited_by_count":212},{"id":"2102.04803","title":"DetCo: Unsupervised Contrastive Learning for Object Detection","created":"2021-02-09","cited_by_count":205},{"id":"2012.08565","title":"Personalized Federated Learning with First Order Model Optimization","created":"2020-12-15","cited_by_count":201},{"id":"2103.00113","title":"Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning","created":"2021-02-27","cited_by_count":177},{"id":"2102.11005","title":"LogME: Practical Assessment of Pre-trained Models for Transfer Learning","created":"2021-02-22","cited_by_count":172},{"id":"2203.09249","title":"Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning","created":"2022-03-17","cited_by_count":172},{"id":"2108.10749","title":"Federated Learning for Open Banking","created":"2021-08-24","cited_by_count":141},{"id":"2010.02372","title":"Lower Bounds and Optimal Algorithms for Personalized Federated Learning","created":"2020-10-05","cited_by_count":123},{"id":"2106.13423","title":"Federated Graph Classification over Non-IID Graphs","created":"2021-06-25","cited_by_count":114},{"id":"2201.06367","title":"Towards Unsupervised Deep Graph Structure Learning","created":"2022-01-17","cited_by_count":114},{"id":"2108.08647","title":"Multi-Center Federated Learning: Clients Clustering for Better Personalization","created":"2021-08-19","cited_by_count":113},{"id":"2106.06047","title":"Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning","created":"2021-06-10","cited_by_count":111},{"id":"2111.02862","title":"Parameterized Knowledge Transfer for Personalized Federated Learning","created":"2021-11-04","cited_by_count":109},{"id":"2010.08982","title":"Federated Unsupervised Representation Learning","created":"2020-10-18","cited_by_count":93},{"id":"2109.12273","title":"FedProc: Prototypical Contrastive Federated Learning on Non-IID data","created":"2021-09-25","cited_by_count":81},{"id":"2206.01535","title":"Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination","created":"2022-06-03","cited_by_count":69},{"id":"2006.11702","title":"A Universal Representation Transformer Layer for Few-Shot Image Classification","created":"2020-06-21","cited_by_count":65},{"id":"1510.07493","title":"Aggregating Deep Convolutional Features for Image Retrieval","created":"2015-10-26","cited_by_count":63},{"id":"1511.08198","title":"Towards Universal Paraphrastic Sentence Embeddings","created":"2015-11-25","cited_by_count":60},{"id":"2202.06187","title":"On the Convergence of Clustered Federated Learning","created":"2022-02-13","cited_by_count":31},{"id":"1909.05024","title":"Learning to Propagate for Graph Meta-Learning","created":"2019-09-11","cited_by_count":29},{"id":"2110.04403","title":"Temperature as Uncertainty in Contrastive Learning","created":"2021-10-08","cited_by_count":25},{"id":"2105.08982","title":"Prototype Guided Federated Learning of Visual Feature Representations","created":"2021-05-19","cited_by_count":22},{"id":"2205.14926","title":"CalFAT: Calibrated Federated Adversarial Training with Label Skewness","created":"2022-05-30","cited_by_count":20},{"id":"2111.10698","title":"Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming","created":"2021-11-20","cited_by_count":15},{"id":"2205.10110","title":"FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels","created":"2022-05-20","cited_by_count":12},{"id":"2012.00925","title":"SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning","created":"2020-12-02","cited_by_count":11},{"id":"2108.07313","title":"Federated Asymptotics: a model to compare federated learning algorithms","created":"2021-08-16","cited_by_count":8},{"id":"2108.10761","title":"Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health","created":"2021-08-24","cited_by_count":8},{"id":"2011.03154","title":"Confusable Learning for Large-class Few-Shot Classification","created":"2020-11-06","cited_by_count":1}],"next_cursor":null}