{"list":"other","total":88,"rows":[{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"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":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1804.07461","title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding","created":"2018-04-20","cited_by_count":4899},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"2101.00027","title":"The Pile: An 800GB Dataset of Diverse Text for Language Modeling","created":"2020-12-31","cited_by_count":2411},{"id":"1509.01626","title":"Character-level Convolutional Networks for Text Classification","created":"2015-09-04","cited_by_count":2330},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"2312.10997","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","created":"2023-12-18","cited_by_count":2212},{"id":"1610.02413","title":"Equality of Opportunity in Supervised Learning","created":"2016-10-07","cited_by_count":2007},{"id":"2210.17323","title":"GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers","created":"2022-10-31","cited_by_count":1919},{"id":"2306.00978","title":"AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration","created":"2023-06-01","cited_by_count":1298},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"1703.02910","title":"Deep Bayesian Active Learning with Image Data","created":"2017-03-08","cited_by_count":819},{"id":"1812.01097","title":"LEAF: A Benchmark for Federated Settings","created":"2018-12-03","cited_by_count":792},{"id":"2202.07646","title":"Quantifying Memorization Across Neural Language Models","created":"2022-02-15","cited_by_count":763},{"id":"2301.13188","title":"Extracting Training Data from Diffusion Models","created":"2023-01-30","cited_by_count":732},{"id":"1907.00503","title":"Modeling Tabular data using Conditional GAN","created":"2019-07-01","cited_by_count":699},{"id":"2304.14108","title":"DataComp: In search of the next generation of multimodal datasets","created":"2023-04-27","cited_by_count":644},{"id":"1906.03671","title":"Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds","created":"2019-06-09","cited_by_count":610},{"id":"2107.07075","title":"Deep Learning on a Data Diet: Finding Important Examples Early in Training","created":"2021-07-15","cited_by_count":533},{"id":"2006.05929","title":"Dataset Condensation with Gradient Matching","created":"2020-06-10","cited_by_count":516},{"id":"2108.04884","title":"Retiring Adult: New Datasets for Fair Machine Learning","created":"2021-08-10","cited_by_count":449},{"id":"2203.11932","title":"Dataset Distillation by Matching Training Trajectories","created":"2022-03-22","cited_by_count":442},{"id":"1702.08423","title":"Age Progression/Regression by Conditional Adversarial Autoencoder","created":"2017-02-27","cited_by_count":429},{"id":"2105.03075","title":"A Survey of Data Augmentation Approaches for NLP","created":"2021-05-07","cited_by_count":409},{"id":"2406.11794","title":"DataComp-LM: In search of the next generation of training sets for language models","created":"2024-06-17","cited_by_count":398},{"id":"2110.04181","title":"Dataset Condensation with Distribution Matching","created":"2021-10-08","cited_by_count":365},{"id":"1802.06309","title":"Learning Adversarially Fair and Transferable Representations","created":"2018-02-17","cited_by_count":353},{"id":"2304.08466","title":"Synthetic Data from Diffusion Models Improves ImageNet Classification","created":"2023-04-17","cited_by_count":327},{"id":"2208.11580","title":"Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning","created":"2022-08-24","cited_by_count":322},{"id":"2503.18813","title":"Defeating Prompt Injections by Design","created":"2025-03-24","cited_by_count":297},{"id":"2308.07633","title":"A Survey on Model Compression for Large Language Models","created":"2023-08-15","cited_by_count":269},{"id":"2402.07867","title":"PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models","created":"2024-02-12","cited_by_count":267},{"id":"1802.06739","title":"Differentially Private Generative Adversarial Network","created":"2018-02-19","cited_by_count":235},{"id":"2011.11660","title":"Differentially Private Learning Needs Better Features (or Much More Data)","created":"2020-11-23","cited_by_count":235},{"id":"1811.10959","title":"Dataset Distillation","created":"2018-11-27","cited_by_count":230},{"id":"2310.17884","title":"Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory","created":"2023-10-27","cited_by_count":230},{"id":"1511.00830","title":"The Variational Fair Autoencoder","created":"2015-11-03","cited_by_count":223},{"id":"1801.05507","title":"Gazelle: A Low Latency Framework for Secure Neural Network Inference","created":"2018-01-16","cited_by_count":193},{"id":"1511.05897","title":"Censoring Representations with an Adversary","created":"2015-11-18","cited_by_count":192},{"id":"1611.04482","title":"Practical Secure Aggregation for Federated Learning on User-Held Data","created":"2016-11-14","cited_by_count":178},{"id":"2003.08673","title":"Survey of Personalization Techniques for Federated Learning","created":"2020-03-19","cited_by_count":145},{"id":"2112.02918","title":"When the Curious Abandon Honesty: Federated Learning Is Not Private","created":"2021-12-06","cited_by_count":127},{"id":"2006.07253","title":"Dynamic Model Pruning with Feedback","created":"2020-06-12","cited_by_count":125},{"id":"2301.05603","title":"A Comprehensive Survey of Dataset Distillation","created":"2023-01-13","cited_by_count":115},{"id":"1805.09458","title":"Invariant Representations without Adversarial Training","created":"2018-05-24","cited_by_count":112},{"id":"2011.07018","title":"Synthetic Data -- Anonymisation Groundhog Day","created":"2020-11-13","cited_by_count":108},{"id":"1910.02551","title":"Soft-Label Dataset Distillation and Text Dataset Distillation","created":"2019-10-06","cited_by_count":107},{"id":"2210.17546","title":"Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy","created":"2022-10-31","cited_by_count":100},{"id":"2405.05175","title":"AirGapAgent: Protecting Privacy-Conscious Conversational Agents","created":"2024-05-08","cited_by_count":94},{"id":"2304.08442","title":"The MiniPile Challenge for Data-Efficient Language Models","created":"2023-04-17","cited_by_count":75},{"id":"2004.02229","title":"FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning","created":"2020-04-05","cited_by_count":74},{"id":"1509.07577","title":"A Review of Feature Selection Methods Based on Mutual Information","created":"2015-09-24","cited_by_count":67},{"id":"2309.06746","title":"DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass","created":"2023-09-13","cited_by_count":67},{"id":"1812.10659","title":"Low Latency Privacy Preserving Inference","created":"2018-12-27","cited_by_count":57},{"id":"2106.07229","title":"Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network","created":"2021-06-14","cited_by_count":57},{"id":"1606.07550","title":"SNAP: A General Purpose Network Analysis and Graph Mining Library","created":"2016-06-24","cited_by_count":56},{"id":"2405.00334","title":"A Survey on Deep Active Learning: Recent Advances and New Frontiers","created":"2024-05-01","cited_by_count":54},{"id":"1710.00901","title":"Prochlo: Strong Privacy for Analytics in the Crowd","created":"2017-10-02","cited_by_count":51},{"id":"2202.08187","title":"Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey","created":"2022-02-16","cited_by_count":47},{"id":"2202.03335","title":"Membership Inference Attacks and Defenses in Neural Network Pruning","created":"2022-02-07","cited_by_count":45},{"id":"2106.05937","title":"Fair Normalizing Flows","created":"2021-06-10","cited_by_count":38},{"id":"2408.02373","title":"Operationalizing Contextual Integrity in Privacy-Conscious Assistants","created":"2024-08-05","cited_by_count":37},{"id":"1606.05893","title":"You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors","created":"2016-06-19","cited_by_count":34},{"id":"2207.00422","title":"Personalized Showcases: Generating Multi-Modal Explanations for Recommendations","created":"2022-06-30","cited_by_count":30},{"id":"1911.07101","title":"Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data","created":"2019-11-16","cited_by_count":28},{"id":"2209.14987","title":"No Free Lunch in \"Privacy for Free: How does Dataset Condensation Help Privacy\"","created":"2022-09-29","cited_by_count":23},{"id":"2205.13316","title":"Fair Representation Learning through Implicit Path Alignment","created":"2022-05-26","cited_by_count":22},{"id":"2008.04113","title":"Data Minimization for GDPR Compliance in Machine Learning Models","created":"2020-08-06","cited_by_count":21},{"id":"2210.07213","title":"FARE: Provably Fair Representation Learning with Practical Certificates","created":"2022-10-13","cited_by_count":21},{"id":"2202.02943","title":"Learning fair representation with a parametric integral probability metric","created":"2022-02-07","cited_by_count":20},{"id":"1909.12732","title":"Alleviating Privacy Attacks via Causal Learning","created":"2019-09-27","cited_by_count":19},{"id":"2005.13718","title":"Operationalizing the Legal Principle of Data Minimization for Personalization","created":"2020-05-28","cited_by_count":19},{"id":"2003.01876","title":"Privacy-preserving Learning via Deep Net Pruning","created":"2020-03-04","cited_by_count":12},{"id":"2305.13917","title":"Generating Data for Symbolic Language with Large Language Models","created":"2023-05-23","cited_by_count":12},{"id":"2107.08096","title":"Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization","created":"2021-07-16","cited_by_count":9},{"id":"2405.19471","title":"The Data Minimization Principle in Machine Learning","created":"2024-05-29","cited_by_count":7},{"id":"2005.09209","title":"Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy","created":"2020-05-19","cited_by_count":6},{"id":"2012.13552","title":"Neural Network Training With Homomorphic Encryption","created":"2020-12-25","cited_by_count":6},{"id":"2305.17593","title":"Data Minimization at Inference Time","created":"2023-05-27","cited_by_count":4},{"id":"2311.10500","title":"From Principle to Practice: Vertical Data Minimization for Machine Learning","created":"2023-11-17","cited_by_count":3},{"id":"2304.07234","title":"Can sparsity improve the privacy of neural networks?","created":"2023-04-11","cited_by_count":1}],"next_cursor":null}