Search Results for author:
Found 22 papers, 7 papers with code
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs.
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks.
A Survey of Graph Unlearning
By laying a solid foundation and fostering continued progress, this survey seeks to inspire researchers to further advance the field of graph unlearning, thereby instilling confidence in the ethical growth of AI systems and reinforcing the responsible application of machine learning techniques in various domains.
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking.
Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks
The minimum dominating set problem seeks to find a dominating set of minimum cardinality and is a well-established NP-hard combinatorial optimization problem.
Sequential Graph Neural Networks for Source Code Vulnerability Identification
However, this task is rather challenging owing to the absence of reliable and adequately managed datasets and learning models.
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
We hypothesize that convolution with LK sizes is limited to maintain an optimal convergence for locality learning.
Open Set Recognition using Vision Transformer with an Additional Detection Head
However, in a more realistic open set scenario, traditional classifiers with incomplete knowledge cannot tackle test data that are not from the training classes.
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning
Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS).
Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning
In this paper, we use the Inductive Venn Predictors framework for computing probability intervals regarding the correctness of each prediction in real-time.
Improving Prediction Confidence in Learning-Enabled Autonomous Systems
In this paper, we utilize a feedback loop between learning-enabled components used for classification and the sensors of an autonomous system in order to improve the confidence of the predictions.
Edge Augmentation with Controllability Constraints in Directed Laplacian Networks
In this paper, we study the maximum edge augmentation problem in directed Laplacian networks to improve their robustness while preserving lower bounds on their strong structural controllability (SSC).
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression
Cyber-physical systems (CPSs) use learning-enabled components (LECs) extensively to cope with various complex tasks under high-uncertainty environments.
Byzantine Resilient Distributed Multi-Task Learning
We analyze the approach for convex models and show that normal agents converge resiliently towards the global minimum. Further, aggregation with the proposed weight assignment rule always results in an improved expected regret than the non-cooperative case.
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression
Learning-enabled components (LECs) are widely used in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy.
Resilient Distributed Vector Consensus Using Centerpoints
In this paper, we study the resilient vector consensus problem in networks with adversarial agents and improve resilience guarantees of existing algorithms.
Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems
Cyber-physical systems (CPS) can benefit by the use of learning enabled components (LECs) such as deep neural networks (DNNs) for perception and decision making tasks.
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems
The simulation results show very small number of false positives and detection delay while the execution time is comparable to the execution time of the original machine learning components.
Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components
In this paper, we investigate how to use the conformal prediction framework for assurance monitoring of CPS with machine learning components.
Computation of the Distance-based Bound on Strong Structural Controllability in Networks
The bound is based on a sequence of vectors containing the distances between leaders (nodes with external inputs) and followers (remaining nodes) in the underlying network graph.
Adversarial Regression for Detecting Attacks in Cyber-Physical Systems
Attacks in cyber-physical systems (CPS) which manipulate sensor readings can cause enormous physical damage if undetected.
Optimal Detection of Faulty Traffic Sensors Used in Route Planning
Erroneous data can adversely affect applications such as route planning, and can cause increased travel time.
