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Found 14 papers, 7 papers with code
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI
The prevailing approaches in Network Intrusion Detection Systems (NIDS) are often hampered by issues such as high resource consumption, significant computational demands, and poor interpretability.
Enhancing object detection robustness: A synthetic and natural perturbation approach
Robustness against real-world distribution shifts is crucial for the successful deployment of object detection models in practical applications.
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
In this paper, we investigate the potential of sparse network structures to flexibly trade-off model storage costs and inference run time against predictive performance and uncertainty quantification ability.
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
We then develop a framework EDITS to mitigate the bias in attributed networks while maintaining the performance of GNNs in downstream tasks.
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo
Federated learning performed by a decentralized networks of agents is becoming increasingly important with the prevalence of embedded software on autonomous devices.
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
Existing efforts can be mainly categorized as spectral-based and spatial-based methods.
Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization
In this paper, we propose a surrogate-assisted evolutionary algorithm (EA) for hyperparameter optimization of machine learning (ML) models.
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) approach that exhibits favourable exploration properties in high-dimensional models such as neural networks.
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
In this paper, we describe initial work on the development ofURSABench(the Uncertainty, Robustness, Scalability, and Accu-racy Benchmark), an open-source suite of bench-marking tools for comprehensive assessment of approximate Bayesian inference methods with a focus on deep learning-based classification tasks
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network classifier, extending prior work on the Bayesian Dark Knowledge framework.
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
In this paper, we consider the problem of assessing the adversarial robustness of deep neural network models under both Markov chain Monte Carlo (MCMC) and Bayesian Dark Knowledge (BDK) inference approximations.
Attribution-Based Confidence Metric For Deep Neural Networks
These experiments demonstrate the effectiveness of the ABC metric to make DNNs more trustworthy and resilient.
Are Graph Neural Networks Miscalibrated?
Graph Neural Networks (GNNs) have proven to be successful in many classification tasks, outperforming previous state-of-the-art methods in terms of accuracy.
Attribution-driven Causal Analysis for Detection of Adversarial Examples
We study the robustness of machine learning models on benign and adversarial inputs in this neighborhood.
