
On ℓ_pnorm Robustness of Ensemble Stumps and Trees
Recent papers have demonstrated that ensemble stumps and trees could be ...
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The Limit of the Batch Size
Largebatch training is an efficient approach for current distributed de...
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Spanning Attack: Reinforce Blackbox Attacks with Unlabeled Data
Adversarial blackbox attacks aim to craft adversarial perturbations by ...
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Towards Nontaskspecific Distillation of BERT via Sentence Representation Approximation
Recently, BERT has become an essential ingredient of various NLP deep mo...
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Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations
Deep Reinforcement Learning (DRL) is vulnerable to small adversarial per...
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Automatic Perturbation Analysis on General Computational Graphs
Linear relaxation based perturbation analysis for neural networks, which...
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Robustness Verification for Transformers
Robustness verification that aims to formally certify the prediction beh...
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MACER: Attackfree and Scalable Robust Training via Maximizing Certified Radius
Adversarial training is one of the most popular ways to learn robust mod...
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MFPN: A Novel Mixture Feature Pyramid Network of Multiple Architectures for Object Detection
Feature pyramids are widely exploited in many detectors to solve the sca...
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Robust TripleMatrixRecoveryBased AutoWeighted Label Propagation for Classification
The graphbased semisupervised label propagation algorithm has delivere...
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Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Recent improvements in largescale language models have driven progress ...
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Enhancing Certifiable Robustness via a Deep Model Ensemble
We propose an algorithm to enhance certified robustness of a deep model ...
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MemeFaceGenerator: Adversarial Synthesis of Chinese Memeface from Natural Sentences
Chinese memeface is a special kind of internet subculture widely spread...
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Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and po...
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Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Training neural networks with verifiable robustness guarantees is challe...
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Robustness Verification of Treebased Models
We study the robustness verification problem for treebased models, incl...
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Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Recent works have shown the effectiveness of randomized smoothing as a s...
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Asymptotic Outage Analysis of Spatially Correlated Rayleigh MIMO Channels
The outage performance of multipleinput multipleoutput (MIMO) techniqu...
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Evaluating Robustness of Deep Image SuperResolution against Adversarial Attacks
Singleimage superresolution aims to generate a highresolution version...
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Second Rethinking of Network Pruning in the Adversarial Setting
It is well known that deep neural networks (DNNs) are vulnerable to adve...
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Robust Decision Trees Against Adversarial Examples
Although adversarial examples and model robustness have been extensively...
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A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Verification of neural networks enables us to gauge their robustness aga...
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A Convex Relaxation Barrier to Tight Robust Verification of Neural Networks
Verification of neural networks enables us to gauge their robustness aga...
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The Limitations of Adversarial Training and the BlindSpot Attack
The adversarial training procedure proposed by Madry et al. (2018) is on...
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Localization from Incomplete Euclidean Distance Matrix: Performance Analysis for the SVDMDS Approach
Localizing a cloud of points from noisy measurements of a subset of pair...
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Efficient Neural Network Robustness Certification with General Activation Functions
Finding minimum distortion of adversarial examples and thus certifying r...
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RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications
The Jacobian matrix (or the gradient for singleoutput networks) is dire...
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On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
CLEVER (CrossLipschitz Extreme Value for nEtwork Robustness) is an Extr...
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Is Robustness the Cost of Accuracy?  A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the longlasting and sole standard for ...
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Structured Adversarial Attack: Towards General Implementation and Better Interpretability
When generating adversarial examples to attack deep neural networks (DNN...
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QueryEfficient Hardlabel Blackbox Attack:An Optimizationbased Approach
We study the problem of attacking a machine learning model in the hardl...
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AutoZOOM: Autoencoderbased Zeroth Order Optimization Method for Attacking Blackbox Neural Networks
Recent studies have shown that adversarial examples in stateoftheart ...
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Towards Fast Computation of Certified Robustness for ReLU Networks
Verifying the robustness property of a general Rectified Linear Unit (Re...
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Seq2Sick: Evaluating the Robustness of SequencetoSequence Models with Adversarial Examples
Crafting adversarial examples has become an important technique to evalu...
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Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
The robustness of neural networks to adversarial examples has received g...
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Phase Transition of Convex Programs for Linear Inverse Problems with Multiple Prior Constraints
A sharp phase transition emerges in convex programs when solving the lin...
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ShowandFool: Crafting Adversarial Examples for Neural Image Captioning
Modern neural image captioning systems typically adopt the encoderdecod...
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Towards Robust Neural Networks via Random Selfensemble
Recent studies have revealed the vulnerability of deep neural networks ...
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EAD: ElasticNet Attacks to Deep Neural Networks via Adversarial Examples
Recent studies have highlighted the vulnerability of deep neural network...
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ZOO: Zeroth Order Optimization based Blackbox Attacks to Deep Neural Networks without Training Substitute Models
Deep neural networks (DNNs) are one of the most prominent technologies o...
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GPUacceleration for Largescale Tree Boosting
In this paper, we present a novel massively parallel algorithm for accel...
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Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Most distributed machine learning systems nowadays, including TensorFlow...
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Huan Zhang
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