Papers Network Interpretation
“Network Interpretation” 태그가 달린 논문 26편 · 필터 해제
A brain-inspired generative model for EEG-based cognitive state identification
This article proposes a brain-inspired generative (BIG) model that merges an impulsive-attention neural network and a variational autoencoder (VAE) for identifying cognitive states based on electroencephalography (EEG) d…
EEGNetwork InterpretationPerturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
The inherent "black box" nature of deep neural networks (DNNs) compromises their transparency and reliability. Recently, explainable AI (XAI) has garnered increasing attention from researchers. Several perturbation-based…
Network InterpretationUnsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
Several natural phenomena and complex systems are often represented as networks. Discovering their community structure is a fundamental task for understanding these networks. Many algorithms have been proposed, but recen…
AttributeClusteringCommunity DetectionGraph Attention+4Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in q…
Network Interpretationparameter estimationQuantitative MRITowards More Robust Interpretation via Local Gradient Alignment
Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several methods for enhancing the local smoothnes…
Computational EfficiencyNetwork InterpretationPlug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contras…
Image CaptioningNetwork InterpretationQuestion AnsweringVisual Question Answering+1Towards Faithful and Consistent Explanations for Graph Neural Networks
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, …
Inductive BiasNetwork InterpretationReflash Dropout in Image Super-Resolution
Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a differen…
Common Sense ReasoningImage Super-ResolutionNetwork InterpretationSuper-ResolutionVisGraphNet: a complex network interpretation of convolutional neural features
Here we propose and investigate the use of visibility graphs to model the feature map of a neural network. The model, initially devised for studies on complex networks, is employed here for the classification of texture …
ClassificationNetwork InterpretationTexture ClassificationDeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation
We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing…
Network InterpretationLatent Map Gaussian Processes for Mixed Variable Metamodeling
Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. In this paper, we introduce latent map Gaussian proces…
Bayesian OptimizationGaussian ProcessesNetwork InterpretationAttribution Preservation in Network Compression for Reliable Network Interpretation
Neural networks embedded in safety-sensitive applications such as self-driving cars and wearable health monitors rely on two important techniques: input attribution for hindsight analysis and network compression to reduc…
Edge-computingNetwork InterpretationSelf-Driving CarsProper Network Interpretability Helps Adversarial Robustness in Classification
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itse…
Adversarial RobustnessClassificationGeneral ClassificationNetwork Interpretation+1Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications
Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, a…
General Classificationimage-classificationImage ClassificationNetwork Interpretation+1On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosi…
Decision Makingimage-classificationImage ClassificationNetwork InterpretationAn Empirical Study on the Relation between Network Interpretability and Adversarial Robustness
Deep neural networks (DNNs) have had many successes, but they suffer from two major issues: (1) a vulnerability to adversarial examples and (2) a tendency to elude human interpretation. Interestingly, recent empirical an…
Adversarial RobustnessImage ClassificationNetwork InterpretationRelationPhysically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such,…
Network InterpretationNeural network interpretation using descrambler groups
The lack of interpretability and trust is a much-criticised feature of deep neural networks. In fully connected nets, the signalling between inner layers is scrambled because backpropagation training does not require per…
Network InterpretationInterpretable Neural Network Decoupling
The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the exploitation of their full potential. Tow…
Network InterpretationA Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction
In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image as an input, the TML raises each pixel va…
Network Interpretation