{"list":"other","total":78,"rows":[{"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":"2205.01068","title":"OPT: Open Pre-trained Transformer Language Models","created":"2022-05-02","cited_by_count":3512},{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"2012.07805","title":"Extracting Training Data from Large Language Models","created":"2020-12-14","cited_by_count":2189},{"id":"1910.02054","title":"ZeRO: Memory Optimizations Toward Training Trillion Parameter Models","created":"2019-10-04","cited_by_count":1301},{"id":"2301.13188","title":"Extracting Training Data from Diffusion Models","created":"2023-01-30","cited_by_count":732},{"id":"2110.05679","title":"Large Language Models Can Be Strong Differentially Private Learners","created":"2021-10-12","cited_by_count":412},{"id":"2110.06500","title":"Differentially Private Fine-tuning of Language Models","created":"2021-10-13","cited_by_count":400},{"id":"2007.14321","title":"Label-Only Membership Inference Attacks","created":"2020-07-28","cited_by_count":388},{"id":"2101.06840","title":"ZeRO-Offload: Democratizing Billion-Scale Model Training","created":"2021-01-18","cited_by_count":341},{"id":"1906.00091","title":"Deep Learning Recommendation Model for Personalization and Recommendation Systems","created":"2019-05-31","cited_by_count":327},{"id":"1311.0776","title":"The Composition Theorem for Differential Privacy","created":"2013-11-04","cited_by_count":297},{"id":"1407.6981","title":"RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response","created":"2014-07-25","cited_by_count":272},{"id":"2104.07857","title":"ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning","created":"2021-04-16","cited_by_count":256},{"id":"1706.09254","title":"The E2E Dataset: New Challenges For End-to-End Generation","created":"2017-06-28","cited_by_count":253},{"id":"2011.11660","title":"Differentially Private Learning Needs Better Features (or Much More Data)","created":"2020-11-23","cited_by_count":235},{"id":"2204.13650","title":"Unlocking High-Accuracy Differentially Private Image Classification through Scale","created":"2022-04-28","cited_by_count":224},{"id":"2101.04535","title":"Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning","created":"2021-01-11","cited_by_count":199},{"id":"2103.00039","title":"Practical and Private (Deep) Learning without Sampling or Shuffling","created":"2021-02-26","cited_by_count":170},{"id":"2111.08440","title":"On the Importance of Difficulty Calibration in Membership Inference Attacks","created":"2021-11-15","cited_by_count":147},{"id":"2007.14191","title":"Tempered Sigmoid Activations for Deep Learning with Differential Privacy","created":"2020-07-28","cited_by_count":122},{"id":"1808.06651","title":"Privacy Amplification by Iteration","created":"2018-08-20","cited_by_count":100},{"id":"2309.11765","title":"Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation","created":"2023-09-21","cited_by_count":96},{"id":"2206.07136","title":"Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger","created":"2022-06-14","cited_by_count":88},{"id":"2305.18465","title":"Federated Learning of Gboard Language Models with Differential Privacy","created":"2023-05-29","cited_by_count":66},{"id":"2210.00038","title":"Differentially Private Optimization on Large Model at Small Cost","created":"2022-09-30","cited_by_count":65},{"id":"2001.02772","title":"DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference","created":"2020-01-08","cited_by_count":54},{"id":"2212.01539","title":"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping","created":"2022-12-03","cited_by_count":52},{"id":"1912.12953","title":"RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing","created":"2019-12-30","cited_by_count":49},{"id":"2306.08153","title":"(Amplified) Banded Matrix Factorization: A unified approach to private training","created":"2023-06-13","cited_by_count":49},{"id":"2205.10683","title":"Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy","created":"2022-05-21","cited_by_count":48},{"id":"2211.06530","title":"Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning","created":"2022-11-12","cited_by_count":48},{"id":"2009.03106","title":"Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping","created":"2020-09-07","cited_by_count":47},{"id":"2201.12383","title":"Bounding Training Data Reconstruction in Private (Deep) Learning","created":"2022-01-28","cited_by_count":46},{"id":"2302.07225","title":"Bounding Training Data Reconstruction in DP-SGD","created":"2023-02-14","cited_by_count":46},{"id":"1709.02753","title":"Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12","created":"2017-09-08","cited_by_count":43},{"id":"1908.03072","title":"TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning","created":"2019-08-08","cited_by_count":43},{"id":"2412.04697","title":"Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy","created":"2024-12-06","cited_by_count":39},{"id":"2102.00075","title":"RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference","created":"2021-01-29","cited_by_count":35},{"id":"2402.13659","title":"Privacy-Preserving Instructions for Aligning Large Language Models","created":"2024-02-21","cited_by_count":35},{"id":"2409.01141","title":"Duplex: A Device for Large Language Models with Mixture of Experts, Grouped Query Attention, and Continuous Batching","created":"2024-09-02","cited_by_count":34},{"id":"2201.00971","title":"Submix: Practical Private Prediction for Large-Scale Language Models","created":"2022-01-04","cited_by_count":31},{"id":"2209.05578","title":"Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis","created":"2022-09-12","cited_by_count":29},{"id":"2407.07737","title":"Fine-Tuning Large Language Models with User-Level Differential Privacy","created":"2024-07-10","cited_by_count":29},{"id":"2312.14334","title":"DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)","created":"2023-12-21","cited_by_count":28},{"id":"2310.09639","title":"DPZero: Private Fine-Tuning of Language Models without Backpropagation","created":"2023-10-14","cited_by_count":26},{"id":"2406.14322","title":"Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning","created":"2024-06-20","cited_by_count":24},{"id":"2310.06771","title":"Correlated Noise Provably Beats Independent Noise for Differentially Private Learning","created":"2023-10-10","cited_by_count":23},{"id":"2007.05089","title":"The Trade-Offs of Private Prediction","created":"2020-07-09","cited_by_count":22},{"id":"2501.18914","title":"Scaling Laws for Differentially Private Language Models","created":"2025-01-31","cited_by_count":22},{"id":"2502.07578","title":"PIM Is All You Need: A CXL-Enabled GPU-Free System for Large Language Model Inference","created":"2025-02-11","cited_by_count":22},{"id":"2502.15470","title":"PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System","created":"2025-02-21","cited_by_count":20},{"id":"2302.01463","title":"Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy","created":"2023-02-02","cited_by_count":18},{"id":"2410.06266","title":"Near Exact Privacy Amplification for Matrix Mechanisms","created":"2024-10-08","cited_by_count":18},{"id":"2210.13662","title":"Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano","created":"2022-10-24","cited_by_count":17},{"id":"2306.08842","title":"ViP: A Differentially Private Foundation Model for Computer Vision","created":"2023-06-15","cited_by_count":17},{"id":"2303.01256","title":"Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance","created":"2023-03-02","cited_by_count":16},{"id":"2411.04205","title":"Scalable DP-SGD: Shuffling vs. Poisson Subsampling","created":"2024-11-06","cited_by_count":16},{"id":"2506.08201","title":"Correlated Noise Mechanisms for Differentially Private Learning","created":"2025-06-09","cited_by_count":14},{"id":"2405.15913","title":"Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML","created":"2024-05-24","cited_by_count":13},{"id":"2410.03883","title":"DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction","created":"2024-10-04","cited_by_count":11},{"id":"2408.08868","title":"A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs","created":"2024-08-16","cited_by_count":9},{"id":"2304.11208","title":"DP-Adam: Correcting DP Bias in Adam's Second Moment Estimation","created":"2023-04-21","cited_by_count":8},{"id":"2404.06430","title":"pfl-research: simulation framework for accelerating research in Private Federated Learning","created":"2024-04-09","cited_by_count":8},{"id":"2311.11822","title":"Zero redundancy distributed learning with differential privacy","created":"2023-11-20","cited_by_count":7},{"id":"2211.11896","title":"Private Ad Modeling with DP-SGD","created":"2022-11-21","cited_by_count":6},{"id":"2401.15246","title":"Training Differentially Private Ad Prediction Models with Semi-Sensitive Features","created":"2024-01-26","cited_by_count":6},{"id":"2411.18752","title":"Locally Differentially Private Online Federated Learning With Correlated Noise","created":"2024-11-27","cited_by_count":6},{"id":"2507.01129","title":"On Design Principles for Private Adaptive Optimizers","created":"2025-07-01","cited_by_count":6},{"id":"2311.08357","title":"Sparsity-Preserving Differentially Private Training of Large Embedding Models","created":"2023-11-14","cited_by_count":5},{"id":"2208.12392","title":"DiVa: An Accelerator for Differentially Private Machine Learning","created":"2022-08-26","cited_by_count":4},{"id":"2503.12314","title":"Empirical Privacy Variance","created":"2025-03-16","cited_by_count":3},{"id":"2510.05288","title":"DP-Adam-AC: Privacy-preserving Fine-Tuning of Localizable Language Models Using Adam Optimization with Adaptive Clipping","created":"2025-10-06","cited_by_count":3},{"id":"2505.16096","title":"Cosmos: A CXL-Based Full In-Memory System for Approximate Nearest Neighbor Search","created":"2025-05-22","cited_by_count":2},{"id":"2404.08847","title":"LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models","created":"2024-04-12","cited_by_count":1},{"id":"2409.16633","title":"PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferences","created":"2024-09-25","cited_by_count":1},{"id":"2505.05648","title":"Privacy-Preserving Transformers: SwiftKey's Differential Privacy Implementation","created":"2025-05-08","cited_by_count":1}],"next_cursor":null}