Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 8 of 8 for “"interpretable deep learning"”.
-
Interpretable Deep Learning for Time Series
… practitioners in such fields are hesitant to use Deep Neural Networks (DNNs) that can be difficult to interpret. For example, in clinical research, one might ask, ``Why did you predict this person as more likely to develop Alzheimer's disease?". As a result, research efforts to improve the …
-
Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations
Deep Neural Network (DNN) models are challenging to interpret because of their highly complex and non-linear nature. This lack of interpretability (1) inhibits adoption within safety critical applications, (2) makes it challenging to debug existing models, and (3) prevents us from extracting …
-
Biologically Interpretable Representation Learning for Mechanistic Insights into Cancer Immunotherapy Resistance
… Disentangled Variational Autoencoder (BDVAE)—an interpretable deep learning framework designed to uncover mechanistic drivers of ICI resistance through multi-omic data integration. Using RNA-seq and wholeexome sequencing data from 366 patients across melanoma, renal cell, urothelial, and gastric …
-
Development of a Fully Automated Al System for Skeletal Maturity Assessment Using Cone Beam CT
… seeks to develop, test, and validate automated, interpretable deep learning algorithms for assessing and classifying the spheno-occipital synchondrosis (SOS) fusion and cervical vertebrae maturity (CVM) from cone beam computed tomography scans. The total data set consisted of 1200 CBCT scans from …
-
Identify Signature Genes/Pathways to Characterize Alzheimer's Disease Subtypes Based on Uncoupled Tauopathies and Cognitive Decline
… so-called atypical AD patients allows for a deeper understanding of possible various disease mechanisms and the factors contributing to disease vulnerability or resilience, which can help guide the drug development and treatment strategy tailored to different subgroups, as well as establish …
-
Predicting Task Functional Localizers Using Naturalistic fMRI
… approaches. This study investigated the use of interpretable deep learning models to predict demographics and functional task localizer activations from fMRI time-series data collected while participants viewed naturalistic stimuli. Using the data of 143 subjects from the Human Connectome …
-
Advancing Catalysis Theory with Theory-infused Deep Learning
… acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. We developed a theory-infused neural network (TinNet) approach that integrates deep …
-
Computational and Data-Driven Design of Perturbed Metal Sites for Catalytic Transformations
… evaluation, surface segregation modeling by deep learning potential-driven molecular simulation and activity prediction through machine learning-embedded electrokinetic model. With this framework, we successfully elucidate the experimentally observed improved activity of PtPdCuNiCo HEA in …