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Found 12 papers, 2 papers with code
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
In many real-world problems, there is a limited set of training data, but an abundance of unlabeled data.
Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems
Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request.
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
This work concerns the development of deep networks that are certifiably robust to adversarial attacks.
Certified robustness against adversarial patch attacks via randomized cropping
This paper proposes a certifiable defense against adversarial patch attacks on image classification.
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries
In this work, we instead show that it is possible to craft (universal) adversarial perturbations in the black-box setting by querying a sequence of different images only once.
Provably robust classification of adversarial examples with detection
Adversarial attacks against deep networks can be defended against either by building robust classifiers or, by creating classifiers that can \emph{detect} the presence of adversarial perturbations.
Certified robustness against physically-realizable patch attack via randomized cropping
Our method improves upon the current state of the art in defending against patch attacks on CIFAR10 and ImageNet, both in terms of certified accuracy and inference time.
Reinforcement Learning for Caching with Space-Time Popularity Dynamics
With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks.
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks
The effectiveness of the novel detectors in the context of competing alternatives is highlighted through extensive tests for various types of adversarial attacks with variable levels of strength.
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing
Under this generic formulation, first by considering stationary distributions for the costs and file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem.
Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities
To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests.
Networking and Internet Architecture
Large-scale Kernel-based Feature Extraction via Budgeted Nonlinear Subspace Tracking
Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks.
