Bio-inspired Analysis of Deep Learning on Not-So-Big Data Using Data-Prototypes
Frontiers in Computational Neuroscience, 2019 Deep artificial neural networks are feed-forward architectures capable of very impressive performances in diverse domains. Indeed stacking multiple layers allows a hierarchical composition of local functions, providing efficient compact mappings ...
Thalita F. Drumond +2 more
doaj +1 more source
Interpretable Predictive Modeling of Tight Gas Well Productivity with SHAP and LIME Techniques
Energies, 2023 Accurately predicting well productivity is crucial for optimizing gas production and maximizing recovery from tight gas reservoirs. Machine learning (ML) techniques have been applied to build predictive models for the well productivity, but their high ...
Xianlin Ma +3 more
doaj +1 more source
Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability
Journal of Metaverse, 2023 Artificial Intelligence (AI) is rapidly integrating into various aspects of our daily lives, influencing decision-making processes in areas such as targeted advertising and matchmaking algorithms. As AI systems become increasingly sophisticated, ensuring
Khaleque Insia +5 more
doaj +1 more source
Imparting Interpretability to Word Embeddings while Preserving Semantic Structure
, 2020 As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation.
Koç, Aykut +4 more
core +2 more sources
T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting
Energies, 2023 Electricity is an essential resource that plays a vital role in modern society, and its demand has increased rapidly alongside industrialization. The accurate forecasting of a country’s electricity demand is crucial for economic development.
Mengkun Liang +4 more
doaj +1 more source
Explainable Machine Learning for Scientific Insights and Discoveries
IEEE Access, 2020 Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences ...
Ribana Roscher +3 more
doaj +1 more source
SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables
Bioengineering, 2023 This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data.
Irfan Al-Hussaini, Cassie S. Mitchell
doaj +1 more source
On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities.
Radiology: Artificial Intelligence, 2020 As artificial intelligence (AI) systems begin to make their way into clinical radiology practice, it is crucial to assure that they function correctly and that they gain the trust of experts.
M. Reyes +7 more
semanticscholar +1 more source
TSInsight: A Local-Global Attribution Framework for Interpretability in Time Series Data
Sensors, 2021 With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time ...
Shoaib Ahmed Siddiqui +3 more
doaj +1 more source
Visual interpretability for deep learning: a survey [PDF]
Frontiers of Information Technology & Electronic Engineering, 2018 This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations.
Quanshi Zhang, Song-Chun Zhu
semanticscholar +1 more source