{"list":"other","total":54,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"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":"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":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"1706.08498","title":"Spectrally-normalized margin bounds for neural networks","created":"2017-06-26","cited_by_count":686},{"id":"1706.08947","title":"Exploring Generalization in Deep Learning","created":"2017-06-27","cited_by_count":600},{"id":"1901.08584","title":"Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks","created":"2019-01-24","cited_by_count":566},{"id":"1509.01240","title":"Train faster, generalize better: Stability of stochastic gradient descent","created":"2015-09-03","cited_by_count":552},{"id":"1703.11008","title":"Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data","created":"2017-03-31","cited_by_count":533},{"id":"1910.00033","title":"Hidden Trigger Backdoor Attacks","created":"2019-09-30","cited_by_count":371},{"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":"1703.02930","title":"Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks","created":"2017-03-08","cited_by_count":311},{"id":"1802.05296","title":"Stronger generalization bounds for deep nets via a compression approach","created":"2018-02-14","cited_by_count":307},{"id":"1902.11237","title":"A new Backdoor Attack in CNNs by training set corruption without label poisoning","created":"2019-02-12","cited_by_count":242},{"id":"2103.09177","title":"Deep learning: a statistical viewpoint","created":"2021-03-16","cited_by_count":240},{"id":"1905.13210","title":"Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks","created":"2019-05-30","cited_by_count":222},{"id":"2301.07067","title":"Transformers as Algorithms: Generalization and Stability in In-context Learning","created":"2023-01-17","cited_by_count":222},{"id":"2101.04898","title":"Unlearnable Examples: Making Personal Data Unexploitable","created":"2021-01-13","cited_by_count":193},{"id":"2005.03823","title":"Blind Backdoors in Deep Learning Models","created":"2020-05-08","cited_by_count":182},{"id":"2110.03735","title":"Adversarial Unlearning of Backdoors via Implicit Hypergradient","created":"2021-10-07","cited_by_count":166},{"id":"2009.02276","title":"Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching","created":"2020-09-04","cited_by_count":163},{"id":"2104.03413","title":"Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective","created":"2021-04-07","cited_by_count":161},{"id":"1906.10842","title":"Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs","created":"2019-06-26","cited_by_count":146},{"id":"1808.10307","title":"Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation","created":"2018-08-30","cited_by_count":134},{"id":"2104.11315","title":"SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics","created":"2021-04-22","cited_by_count":130},{"id":"1909.12292","title":"Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks","created":"2019-09-26","cited_by_count":121},{"id":"2204.05255","title":"Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information","created":"2022-04-11","cited_by_count":103},{"id":"2106.08970","title":"Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch","created":"2021-06-16","cited_by_count":102},{"id":"2302.03251","title":"SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency","created":"2023-02-07","cited_by_count":97},{"id":"1703.01678","title":"Data-Dependent Stability of Stochastic Gradient Descent","created":"2017-03-05","cited_by_count":92},{"id":"2011.09527","title":"Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff","created":"2020-11-18","cited_by_count":89},{"id":"2002.11497","title":"On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping","created":"2020-02-26","cited_by_count":83},{"id":"1905.03684","title":"Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation","created":"2019-05-09","cited_by_count":71},{"id":"2304.11823","title":"Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization","created":"2023-04-24","cited_by_count":64},{"id":"1905.12600","title":"Generalization bounds for deep convolutional neural networks","created":"2019-05-29","cited_by_count":59},{"id":"1911.07399","title":"NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations","created":"2019-11-18","cited_by_count":50},{"id":"2010.12561","title":"Train simultaneously, generalize better: Stability of gradient-based minimax learners","created":"2020-10-23","cited_by_count":44},{"id":"2012.04115","title":"Generalization bounds for deep learning","created":"2020-12-07","cited_by_count":38},{"id":"2203.14533","title":"Robust Unlearnable Examples: Protecting Data Against Adversarial Learning","created":"2022-03-28","cited_by_count":35},{"id":"2210.00960","title":"Stability Analysis and Generalization Bounds of Adversarial Training","created":"2022-10-03","cited_by_count":30},{"id":"1809.03090","title":"Approximation and Estimation for High-Dimensional Deep Learning Networks","created":"2018-09-10","cited_by_count":22},{"id":"2207.09560","title":"Holistic Robust Data-Driven Decisions","created":"2022-07-19","cited_by_count":18},{"id":"2206.03299","title":"Generalization Error Bounds for Deep Neural Networks Trained by SGD","created":"2022-06-07","cited_by_count":14},{"id":"2210.02713","title":"On Optimal Learning Under Targeted Data Poisoning","created":"2022-10-06","cited_by_count":14},{"id":"2312.08898","title":"Detection and Defense of Unlearnable Examples","created":"2023-12-14","cited_by_count":13},{"id":"1910.03624","title":"SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations","created":"2019-10-08","cited_by_count":12},{"id":"2303.02251","title":"Certified Robust Neural Networks: Generalization and Corruption Resistance","created":"2023-03-03","cited_by_count":11},{"id":"2202.06233","title":"The Sample Complexity of One-Hidden-Layer Neural Networks","created":"2022-02-13","cited_by_count":10},{"id":"2206.04881","title":"Enhancing Clean Label Backdoor Attack with Two-phase Specific Triggers","created":"2022-06-10","cited_by_count":7},{"id":"2401.03156","title":"Data-Dependent Stability Analysis of Adversarial Training","created":"2024-01-06","cited_by_count":4}],"next_cursor":null}