{"list":"other","total":51,"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":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"2301.12597","title":"BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models","created":"2023-01-30","cited_by_count":6137},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1812.06127","title":"Federated Optimization in Heterogeneous Networks","created":"2018-12-14","cited_by_count":2987},{"id":"2304.10592","title":"MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models","created":"2023-04-20","cited_by_count":2711},{"id":"1901.05555","title":"Class-Balanced Loss Based on Effective Number of Samples","created":"2019-01-16","cited_by_count":1245},{"id":"2306.05424","title":"Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models","created":"2023-06-08","cited_by_count":1062},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"2007.07481","title":"Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization","created":"2020-07-15","cited_by_count":906},{"id":"2305.06355","title":"VideoChat: Chat-Centric Video Understanding","created":"2023-05-10","cited_by_count":857},{"id":"1909.06335","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","created":"2019-09-13","cited_by_count":851},{"id":"1910.09217","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","created":"2019-10-21","cited_by_count":809},{"id":"2003.14053","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","created":"2020-03-31","cited_by_count":712},{"id":"1904.05160","title":"Large-Scale Long-Tailed Recognition in an Open World","created":"2019-04-10","cited_by_count":705},{"id":"2006.07242","title":"Ensemble Distillation for Robust Model Fusion in Federated Learning","created":"2020-06-12","cited_by_count":682},{"id":"2303.04671","title":"Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models","created":"2023-03-08","cited_by_count":668},{"id":"2311.07919","title":"Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models","created":"2023-11-14","cited_by_count":608},{"id":"2304.15010","title":"LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model","created":"2023-04-28","cited_by_count":602},{"id":"1902.03393","title":"Improved Knowledge Distillation via Teacher Assistant","created":"2019-02-09","cited_by_count":573},{"id":"2303.11381","title":"MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action","created":"2023-03-20","cited_by_count":517},{"id":"1608.04471","title":"Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm","created":"2016-08-16","cited_by_count":507},{"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":"1902.03932","title":"Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning","created":"2019-02-11","cited_by_count":203},{"id":"2305.05644","title":"Towards Building the Federated GPT: Federated Instruction Tuning","created":"2023-05-09","cited_by_count":185},{"id":"1907.09173","title":"FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare","created":"2019-07-22","cited_by_count":182},{"id":"2009.01974","title":"FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning","created":"2020-09-04","cited_by_count":176},{"id":"2206.07758","title":"Reconstructing Training Data from Trained Neural Networks","created":"2022-06-15","cited_by_count":133},{"id":"2111.02862","title":"Parameterized Knowledge Transfer for Personalized Federated Learning","created":"2021-11-04","cited_by_count":109},{"id":"2404.02936","title":"Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models","created":"2024-04-03","cited_by_count":103},{"id":"1904.09149","title":"Knowledge Distillation via Route Constrained Optimization","created":"2019-04-19","cited_by_count":75},{"id":"2104.13417","title":"Towards Fair Federated Learning with Zero-Shot Data Augmentation","created":"2021-04-27","cited_by_count":64},{"id":"2204.13399","title":"Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features","created":"2022-04-28","cited_by_count":63},{"id":"2011.11266","title":"Federated learning with class imbalance reduction","created":"2020-11-23","cited_by_count":52},{"id":"2102.05218","title":"FLOP: Federated Learning on Medical Datasets using Partial Networks","created":"2021-02-10","cited_by_count":42},{"id":"2303.16181","title":"Learning Federated Visual Prompt in Null Space for MRI Reconstruction","created":"2023-03-28","cited_by_count":40},{"id":"2209.15245","title":"Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction","created":"2022-09-30","cited_by_count":32},{"id":"2011.06283","title":"Fed-Focal Loss for imbalanced data classification in Federated Learning","created":"2020-11-12","cited_by_count":26},{"id":"1809.01293","title":"Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory","created":"2018-09-05","cited_by_count":20},{"id":"2210.03277","title":"Rethinking Normalization Methods in Federated Learning","created":"2022-10-07","cited_by_count":19},{"id":"2312.01537","title":"Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents","created":"2023-12-03","cited_by_count":16},{"id":"1811.08052","title":"Variance Reduction in Stochastic Particle-Optimization Sampling","created":"2018-11-20","cited_by_count":11},{"id":"2312.08648","title":"CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data","created":"2023-12-14","cited_by_count":9},{"id":"1811.08929","title":"Self-Adversarially Learned Bayesian Sampling","created":"2018-11-21","cited_by_count":8},{"id":"2106.12300","title":"Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning","created":"2021-06-23","cited_by_count":7},{"id":"2308.06051","title":"Towards Instance-adaptive Inference for Federated Learning","created":"2023-08-11","cited_by_count":7},{"id":"2210.02729","title":"Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer","created":"2022-10-06","cited_by_count":2},{"id":"2311.04799","title":"DA-Cramming: Enhancing Cost-Effective Language Model Pretraining with Dependency Agreement Integration","created":"2023-11-08","cited_by_count":2}],"next_cursor":null}