{"list":"other","total":54,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"2108.07258","title":"On the Opportunities and Risks of Foundation Models","created":"2021-08-16","cited_by_count":4033},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1610.01644","title":"Understanding intermediate layers using linear classifier probes","created":"2016-10-05","cited_by_count":1353},{"id":"1902.02918","title":"Certified Adversarial Robustness via Randomized Smoothing","created":"2019-02-08","cited_by_count":1340},{"id":"2305.10403","title":"PaLM 2 Technical Report","created":"2023-05-17","cited_by_count":1162},{"id":"1711.06025","title":"Learning to Compare: Relation Network for Few-Shot Learning","created":"2017-11-16","cited_by_count":1143},{"id":"1708.06733","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","created":"2017-08-22","cited_by_count":1106},{"id":"1803.01498","title":"Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates","created":"2018-03-05","cited_by_count":711},{"id":"1904.04232","title":"A Closer Look at Few-shot Classification","created":"2019-04-08","cited_by_count":684},{"id":"1805.12185","title":"Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks","created":"2018-05-30","cited_by_count":580},{"id":"1804.00792","title":"Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks","created":"2018-04-03","cited_by_count":519},{"id":"1811.00636","title":"Spectral Signatures in Backdoor Attacks","created":"2018-11-01","cited_by_count":484},{"id":"1802.03471","title":"Certified Robustness to Adversarial Examples with Differential Privacy","created":"2018-02-09","cited_by_count":468},{"id":"1902.06531","title":"STRIP: A Defence Against Trojan Attacks on Deep Neural Networks","created":"2019-02-18","cited_by_count":459},{"id":"1811.03728","title":"Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering","created":"2018-11-09","cited_by_count":456},{"id":"1805.08136","title":"Meta-learning with differentiable closed-form solvers","created":"2018-05-21","cited_by_count":397},{"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":"1706.03691","title":"Certified Defenses for Data Poisoning Attacks","created":"2017-06-09","cited_by_count":303},{"id":"2302.10149","title":"Poisoning Web-Scale Training Datasets is Practical","created":"2023-02-20","cited_by_count":290},{"id":"1708.08689","title":"Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization","created":"2017-08-29","cited_by_count":197},{"id":"1812.00292","title":"SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems","created":"2018-12-02","cited_by_count":152},{"id":"2106.09667","title":"Poisoning and Backdooring Contrastive Learning","created":"2021-06-17","cited_by_count":151},{"id":"1811.00741","title":"Stronger Data Poisoning Attacks Break Data Sanitization Defenses","created":"2018-11-02","cited_by_count":135},{"id":"2006.11165","title":"Backdoor Attacks to Graph Neural Networks","created":"2020-06-19","cited_by_count":128},{"id":"2204.07305","title":"Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference","created":"2022-04-15","cited_by_count":126},{"id":"2003.08904","title":"RAB: Provable Robustness Against Backdoor Attacks","created":"2020-03-19","cited_by_count":118},{"id":"2006.14768","title":"Deep Partition Aggregation: Provable Defense against General Poisoning Attacks","created":"2020-06-26","cited_by_count":115},{"id":"2002.03018","title":"Certified Robustness to Label-Flipping Attacks via Randomized Smoothing","created":"2020-02-07","cited_by_count":108},{"id":"2009.04131","title":"SoK: Certified Robustness for Deep Neural Networks","created":"2020-09-09","cited_by_count":105},{"id":"2008.04495","title":"Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks","created":"2020-08-11","cited_by_count":94},{"id":"1905.06549","title":"TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning","created":"2019-05-16","cited_by_count":91},{"id":"2111.00197","title":"Backdoor Pre-trained Models Can Transfer to All","created":"2021-10-30","cited_by_count":86},{"id":"1910.04749","title":"Defending Neural Backdoors via Generative Distribution Modeling","created":"2019-10-10","cited_by_count":84},{"id":"1903.09860","title":"Data Poisoning against Differentially-Private Learners: Attacks and Defenses","created":"2019-03-23","cited_by_count":78},{"id":"2002.11750","title":"On Certifying Robustness against Backdoor Attacks via Randomized Smoothing","created":"2020-02-26","cited_by_count":77},{"id":"2012.07006","title":"DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation","created":"2020-12-13","cited_by_count":77},{"id":"1905.13409","title":"Bypassing Backdoor Detection Algorithms in Deep Learning","created":"2019-05-31","cited_by_count":74},{"id":"1810.07218","title":"Incremental Few-Shot Learning with Attention Attractor Networks","created":"2018-10-16","cited_by_count":66},{"id":"2102.01854","title":"Provably Secure Federated Learning against Malicious Clients","created":"2021-02-03","cited_by_count":63},{"id":"2012.03765","title":"Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks","created":"2020-12-07","cited_by_count":60},{"id":"2210.00584","title":"FLCert: Provably Secure Federated Learning against Poisoning Attacks","created":"2022-10-02","cited_by_count":30},{"id":"2401.01377","title":"Does Few-shot Learning Suffer from Backdoor Attacks?","created":"2023-12-31","cited_by_count":20},{"id":"2204.14081","title":"Few-shot learning for medical text: A systematic review","created":"2022-04-21","cited_by_count":13},{"id":"2009.01672","title":"Yet Meta Learning Can Adapt Fast, It Can Also Break Easily","created":"2020-09-02","cited_by_count":10},{"id":"2303.14601","title":"PORE: Provably Robust Recommender Systems against Data Poisoning Attacks","created":"2023-03-26","cited_by_count":10},{"id":"2110.15444","title":"10 Security and Privacy Problems in Large Foundation Models","created":"2021-10-28","cited_by_count":6},{"id":"2202.01186","title":"Smoothed Embeddings for Certified Few-Shot Learning","created":"2022-02-02","cited_by_count":6},{"id":"1908.10266","title":"Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition","created":"2019-08-27","cited_by_count":5},{"id":"2211.12990","title":"Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners","created":"2022-11-23","cited_by_count":4}],"next_cursor":null}