Results 51 to 60 of about 1,675,134 (344)

Bio-inspired Analysis of Deep Learning on Not-So-Big Data Using Data-Prototypes

open access: yesFrontiers 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

open access: yesEnergies, 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

open access: yesJournal 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

open access: yes, 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

open access: yesEnergies, 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

open access: yesIEEE 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

open access: yesBioengineering, 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.

open access: yesRadiology: 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

open access: yesSensors, 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]

open access: yesFrontiers 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

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