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Found 20 papers, 7 papers with code
Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning
Our analysis of these two failure cases of DNNs reveals that finding a unified solution for shortcut learning in DNNs is not out of reach, and TDA can play a significant role in forming such a framework.
It's Simplex! Disaggregating Measures to Improve Certified Robustness
Certified robustness circumvents the fragility of defences against adversarial attacks, by endowing model predictions with guarantees of class invariance for attacks up to a calculated size.
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
Poisoning attacks can disproportionately influence model behaviour by making small changes to the training corpus.
Towards quantum enhanced adversarial robustness in machine learning
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge.
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
In guaranteeing the absence of adversarial examples in an instance's neighbourhood, certification mechanisms play an important role in demonstrating neural net robustness.
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation tasks.
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry.
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
In response to subtle adversarial examples flipping classifications of neural network models, recent research has promoted certified robustness as a solution.
Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm
However, the impact of coreset selection on the performance of quantum K-Means clustering has not been explored.
Local Intrinsic Dimensionality Signals Adversarial Perturbations
In this paper, we derive a lower-bound and an upper-bound for the LID value of a perturbed data point and demonstrate that the bounds, in particular the lower-bound, has a positive correlation with the magnitude of the perturbation.
A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences
The results show improvements up to four times compared to the state of the art in suffix and remaining time prediction of event sequences, specifically in the realm of business process executions.
Neural Architecture Search via Combinatorial Multi-Armed Bandit
NAS can be performed via policy gradient, evolutionary algorithms, differentiable architecture search or tree-search methods.
Improving Scalability of Contrast Pattern Mining for Network Traffic Using Closed Patterns
In addition, as an application of CPs, we demonstrate that CPM is a highly effective method for detection of meaningful changes in network traffic.
Defending Regression Learners Against Poisoning Attacks
Regression models, which are widely used from engineering applications to financial forecasting, are vulnerable to targeted malicious attacks such as training data poisoning, through which adversaries can manipulate their predictions.
Defending Distributed Classifiers Against Data Poisoning Attacks
We introduce a weighted SVM against such attacks using K-LID as a distinguishing characteristic that de-emphasizes the effect of suspicious data samples on the SVM decision boundary.
Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs).
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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction.
Online Cluster Validity Indices for Streaming Data
An important part of cluster analysis is validating the quality of computationally obtained clusters.
Toward the Starting Line: A Systems Engineering Approach to Strong AI
After many hype cycles and lessons from AI history, it is clear that a big conceptual leap is needed for crossing the starting line to kick-start mainstream AGI research.
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms
The two new algorithms are heuristic derivatives of fuzzy c-means (FCM).
