Papers Interpretability Techniques for Deep Learning
“Interpretability Techniques for Deep Learning” 태그가 달린 논문 25편 · 필터 해제
Time series saliency maps: explaining models across multiple domains
Traditional saliency map methods, popularized in computer vision, highlight individual points (pixels) of the input that contribute the most to the model's output. However, in time-series they offer limited insights as s…
Explainable Artificial Intelligence (XAI)Interpretability Techniques for Deep LearningPhotoplethysmography (PPG) heart rate estimationSeizure Detection+2Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability
Diffusion models have demonstrated impressive capabilities in synthesizing diverse content. However, despite their high-quality outputs, these models often perpetuate social biases, including those related to gender and …
Age/UnbiasedDecision MakingImage GenerationInterpretability Techniques for Deep Learning+1IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology
Histopathological image analysis is crucial for accurate cancer diagnosis and treatment planning. While deep learning models, especially convolutional neural networks, have advanced this field, their "black-box" nature r…
ClassificationDenoisingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)+1Explainable Deep Learning: A Visual Analytics Approach with Transition Matrices
The non-transparency of artificial intelligence (AI) systems, particularly in deep learning (DL), poses significant challenges to their comprehensibility and trustworthiness. This study aims to enhance the explainability…
Deep LearningExplainable Artificial Intelligence (XAI)Image GenerationInterpretability Techniques for Deep Learning+3CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Language models (LMs) have proven to be powerful tools for psycholinguistic research, but most prior work has focused on purely behavioural measures (e.g., surprisal comparisons). At the same time, research in model inte…
BenchmarkingInterpretability Techniques for Deep LearningLess is More: Fewer Interpretable Region via Submodular Subset Selection
Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face th…
Error UnderstandingImage AttributionInterpretability Techniques for Deep LearningTraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
In Federated Learning, clients train models on local data and send updates to a central server, which aggregates them into a global model using a fusion algorithm. This collaborative yet privacy-preserving training comes…
Explainable ModelsFault localizationFederated Learningimage-classification+6Improving Interpretability via Regularization of Neural Activation Sensitivity
State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two major weaknesses - their susceptibility to…
Adversarial RobustnessExplanation Fidelity EvaluationInterpretability Techniques for Deep LearningSensitivityMaking Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
This paper presents a new efficient black-box attribution method based on Hilbert-Schmidt Independence Criterion (HSIC), a dependence measure based on Reproducing Kernel Hilbert Spaces (RKHS). HSIC measures the dependenc…
Error UnderstandingImage AttributionInterpretability Techniques for Deep Learningobject-detection+1Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we derive a continuous-time model for the dyn…
Graph Neural NetworkInterpretability Techniques for Deep LearningInterpretable Machine LearningSpatio-Temporal Forecasting+1A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19
The COVID-19 pandemic has significantly impacted the tourism and hospitality sector. Public policies such as travel restrictions and stay-at-home orders had significantly affected tourist activities and service businesse…
Correlated Time Series ForecastingCOVID-19 ModellingCOVID-19 TrackingInterpretability Techniques for Deep Learning+7A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction
Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends…
Interpretability Techniques for Deep LearningManagementPrognosisTransfer LearningDISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars. One of the principal b…
counterfactualFairnessInterpretability Techniques for Deep LearningInterpretable Machine LearningA deep supervised learning approach for condition-based maintenance of naval propulsion systems Tarek
In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic m…
Interpretability Techniques for Deep LearningDeepNNK: Explaining deep models and their generalization using polytope interpolation
Modern machine learning systems based on neural networks have shown great success in learning complex data patterns while being able to make good predictions on unseen data points. However, the limited interpretability o…
BIG-bench Machine LearningInterpretability Techniques for Deep LearningInterpretable Machine LearningModel SelectionExploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected pa…
COVID-19 DiagnosisGeneral ClassificationInterpretability Techniques for Deep LearningInterpretable Machine Learning+2An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics
This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. We applied these algorithms in a real-wor…
Decision MakingInterpretability Techniques for Deep LearningPredictionWhat Do Compressed Deep Neural Networks Forget?
Deep neural network pruning and quantization techniques have demonstrated it is possible to achieve high levels of compression with surprisingly little degradation to test set accuracy. However, this measure of performan…
FairnessInterpretability Techniques for Deep LearningModel CompressionNetwork Pruning+2RISE: Randomized Input Sampling for Explanation of Black-box Models
Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to the end users. In this paper, we address…
Explainable Artificial Intelligence (XAI)Feature ImportanceImage AttributionImage Captioning+3Contextual Explanation Networks
Modern learning algorithms excel at producing accurate but complex models of the data. However, deploying such models in the real-world requires extra care: we must ensure their reliability, robustness, and absence of un…
Image ClassificationInterpretability Techniques for Deep LearningPredictionSentiment Analysis+4