{"list":"other","total":56,"rows":[{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2309.06180","title":"Efficient Memory Management for Large Language Model Serving with PagedAttention","created":"2023-09-12","cited_by_count":5944},{"id":"1708.06733","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","created":"2017-08-22","cited_by_count":1106},{"id":"1712.05526","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","created":"2017-12-15","cited_by_count":1055},{"id":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"1902.06531","title":"STRIP: A Defence Against Trojan Attacks on Deep Neural Networks","created":"2019-02-18","cited_by_count":459},{"id":"2012.03816","title":"Invisible Backdoor Attack with Sample-Specific Triggers","created":"2020-12-07","cited_by_count":353},{"id":"2007.02343","title":"Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks","created":"2020-07-05","cited_by_count":349},{"id":"2010.08138","title":"Input-Aware Dynamic Backdoor Attack","created":"2020-10-16","cited_by_count":313},{"id":"1912.02771","title":"Label-Consistent Backdoor Attacks","created":"2019-12-05","cited_by_count":296},{"id":"2006.06762","title":"Ansor: Generating High-Performance Tensor Programs for Deep Learning","created":"2020-06-11","cited_by_count":201},{"id":"1908.00686","title":"Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection","created":"2019-08-02","cited_by_count":124},{"id":"2007.10760","title":"Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review","created":"2020-07-21","cited_by_count":123},{"id":"2006.08131","title":"An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks","created":"2020-06-15","cited_by_count":97},{"id":"1803.06975","title":"Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks","created":"2018-03-19","cited_by_count":81},{"id":"2405.18137","title":"Exploiting LLM Quantization","created":"2024-05-28","cited_by_count":65},{"id":"1907.05587","title":"Stateful Detection of Black-Box Adversarial Attacks","created":"2019-07-12","cited_by_count":56},{"id":"2205.06900","title":"MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic","created":"2022-05-13","cited_by_count":55},{"id":"2203.15859","title":"NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models","created":"2022-03-29","cited_by_count":50},{"id":"2207.04606","title":"SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning","created":"2022-07-11","cited_by_count":47},{"id":"2303.02552","title":"An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry","created":"2023-03-05","cited_by_count":47},{"id":"2110.08450","title":"Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining","created":"2021-10-16","cited_by_count":39},{"id":"2006.14042","title":"Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks","created":"2020-06-24","cited_by_count":37},{"id":"2012.09302","title":"TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors","created":"2020-12-16","cited_by_count":30},{"id":"2207.13066","title":"NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers","created":"2022-07-26","cited_by_count":30},{"id":"2404.01231","title":"Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models","created":"2024-04-01","cited_by_count":30},{"id":"2108.09187","title":"Quantization Backdoors to Deep Learning Commercial Frameworks","created":"2021-08-20","cited_by_count":29},{"id":"2206.07840","title":"Architectural Backdoors in Neural Networks","created":"2022-06-15","cited_by_count":25},{"id":"2202.02326","title":"Towards Training Reproducible Deep Learning Models","created":"2022-02-04","cited_by_count":23},{"id":"2403.09562","title":"PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps","created":"2024-03-14","cited_by_count":23},{"id":"2010.13103","title":"LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference","created":"2020-10-25","cited_by_count":22},{"id":"2011.01302","title":"IOS: Inter-Operator Scheduler for CNN Acceleration","created":"2020-11-02","cited_by_count":20},{"id":"2110.13541","title":"Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes","created":"2021-10-26","cited_by_count":19},{"id":"2011.01383","title":"Cortex: A Compiler for Recursive Deep Learning Models","created":"2020-11-02","cited_by_count":16},{"id":"2205.10364","title":"Learning to Reverse DNNs from AI Programs Automatically","created":"2022-05-20","cited_by_count":12},{"id":"2202.03493","title":"DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep Learning","created":"2022-02-07","cited_by_count":11},{"id":"2402.06957","title":"Architectural Neural Backdoors from First Principles","created":"2024-02-10","cited_by_count":11},{"id":"2111.14255","title":"Automated Runtime-Aware Scheduling for Multi-Tenant DNN Inference on GPU","created":"2021-11-28","cited_by_count":9},{"id":"2210.00108","title":"ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks","created":"2022-09-30","cited_by_count":9},{"id":"2307.04963","title":"DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization","created":"2023-07-11","cited_by_count":9},{"id":"2303.11643","title":"Manipulating Transfer Learning for Property Inference","created":"2023-03-21","cited_by_count":8},{"id":"2105.09377","title":"Pure Tensor Program Rewriting via Access Patterns (Representation Pearl)","created":"2021-05-19","cited_by_count":7},{"id":"2302.02261","title":"NeuRI: Diversifying DNN Generation via Inductive Rule Inference","created":"2023-02-04","cited_by_count":7},{"id":"2312.04902","title":"BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Lifting","created":"2023-12-08","cited_by_count":7},{"id":"2411.05197","title":"Hardware and Software Platform Inference","created":"2024-11-07","cited_by_count":7},{"id":"2204.10933","title":"A Tale of Two Models: Constructing Evasive Attacks on Edge Models","created":"2022-04-22","cited_by_count":6},{"id":"2111.14247","title":"A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities","created":"2021-11-28","cited_by_count":5},{"id":"2308.15039","title":"R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics","created":"2023-08-29","cited_by_count":5},{"id":"2507.08983","title":"Exploiting Leaderboards for Large-Scale Distribution of Malicious Models","created":"2025-07-11","cited_by_count":5},{"id":"2310.20078","title":"TorchProbe: Fuzzing Dynamic Deep Learning Compilers","created":"2023-10-30","cited_by_count":4},{"id":"2404.16947","title":"Fuzzing MLIR Compilers with Custom Mutation Synthesis","created":"2024-04-25","cited_by_count":4},{"id":"2312.10636","title":"Graft: Efficient Inference Serving for Hybrid Deep Learning with SLO Guarantees via DNN Re-alignment","created":"2023-12-17","cited_by_count":3},{"id":"2505.18323","title":"Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation","created":"2025-05-23","cited_by_count":3},{"id":"1705.10416","title":"Finding Root Causes of Floating Point Error with Herbgrind","created":"2017-05-29","cited_by_count":2},{"id":"2102.12782","title":"NSan: A Floating-Point Numerical Sanitizer","created":"2021-02-25","cited_by_count":1},{"id":"2407.16626","title":"A Tale of Two DL Cities: When Library Tests Meet Compiler","created":"2024-07-23","cited_by_count":1}],"next_cursor":null}