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Found 41 papers, 19 papers with code
Provably Robust Metric Learning
Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less studied.
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data
By constraining adversarial perturbations in a low-dimensional subspace via spanning an auxiliary unlabeled dataset, the spanning attack significantly improves the query efficiency of a wide variety of existing black-box attacks.
Improving Adversarial Robustness Requires Revisiting Misclassified Examples
In this paper, we investigate the distinctive influence of misclassified and correctly classified examples on the final robustness of adversarial training.
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems.
DTWNet: a Dynamic Time Warping Network
Dynamic Time Warping (DTW) is widely used as a similarity measure in various domains.
Symmetric Cross Entropy for Robust Learning with Noisy Labels
In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes ("easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes ("hard" classes).
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Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points
In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels.
Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world.
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
Furthermore, we show that dual solutions for these QP problems could give us a valid lower bound of the adversarial perturbation that can be used for formal robustness verification, giving us a nice view of attack/verification for NN models.
Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation aims to transfer the classifier learned from the source domain to the target domain in an unsupervised manner.
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
In this paper, we improve the robustness of DNNs by utilizing techniques of Distance Metric Learning.
Inferring the Importance of Product Appearance: A Step Towards the Screenless Revolution
In this paper, we aim to infer the significance of every item's appearance in consumer decision making and identify the group of items that are suitable for screenless shopping.
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach
We study the problem of attacking machine learning models in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions.
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
To the best of our knowledge, this is the first work to investigate privacy leakage in DRL settings and we show that DRL-based agents do potentially leak privacy-sensitive information from the trained policies.
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
We guide the optimization of label quality through a small amount of validation data, and to ensure the safeness of performance while maximizing performance gain.
Adaptive Negative Curvature Descent with Applications in Non-convex Optimization
Negative curvature descent (NCD) method has been utilized to design deterministic or stochastic algorithms for non-convex optimization aiming at finding second-order stationary points or local minima.
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Depending on how much information an adversary can access to, adversarial attacks can be classified as white-box attack and black-box attack.
Random Warping Series: A Random Features Method for Time-Series Embedding
The proposed kernel does not suffer from the issue of diagonal dominance while naturally enjoys a \emph{Random Features} (RF) approximation, which reduces the computational complexity of existing DTW-based techniques from quadratic to linear in terms of both the number and the length of time-series.
Query-Efficient Black-Box Attack by Active Learning
To conduct black-box attack, a popular approach aims to train a substitute model based on the information queried from the target DNN.
Towards Query Efficient Black-box Attacks: An Input-free Perspective
First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model.
Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
For example, there is still a lack of theories of convergence for SGD and its variants that use stagewise step size and return an averaged solution in practice.
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition.
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions
Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks.
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
We study the problem of attacking a machine learning model in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions.
Matrix Completion from Non-Uniformly Sampled Entries
Then, for each partially observed column, we recover it by finding a vector which lies in the recovered column space and consists of the observed entries.
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms.
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks
Recent studies have shown that adversarial examples in state-of-the-art image classifiers trained by deep neural networks (DNN) can be easily generated when the target model is transparent to an attacker, known as the white-box setting.
Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains.
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities.
Edge Attention-based Multi-Relational Graph Convolutional Networks
The proposed GCN model, which we call edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution.
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
The surging availability of electronic medical records (EHR) leads to increased research interests in medical predictive modeling.
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness.
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning
Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems.
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples - a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to misclassify.
Robust Task Clustering for Deep Many-Task Learning
We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario.
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples.
Similarity Preserving Representation Learning for Time Series Clustering
A considerable amount of clustering algorithms take instance-feature matrices as their inputs.
Improved Dynamic Regret for Non-degenerate Functions
When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded by the minimum of the path-length and the squared path-length.
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
Secondly, we present a lower bound with noisy gradient feedback and then show that we can achieve optimal dynamic regrets under a stochastic gradient feedback and two-point bandit feedback.
Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD)
Although stochastic gradient descent (SGD) has been successfully applied to improve the efficiency of DML, it can still be computationally expensive because in order to ensure that the solution is a PSD matrix, it has to, at every iteration, project the updated distance metric onto the PSD cone, an expensive operation.
Stochastic Gradient Descent with Only One Projection
Although many variants of stochastic gradient descent have been proposed for large-scale convex optimization, most of them require projecting the solution at {\it each} iteration to ensure that the obtained solution stays within the feasible domain.
