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Found 6 papers, 3 papers with code
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.
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying.
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz.
FEAST: An Automated Feature Selection Framework for Compilation Tasks
In this paper, we propose FEAture Selection for compilation Tasks (FEAST), an efficient and automated framework for determining the most relevant and representative features from a feature pool.
When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications.
Supervised Collective Classification for Crowdsourcing
Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e. g., labels of an item) provided by labelers.
