Results 31 to 40 of about 2,756,498 (272)
iCapsNets: Towards Interpretable Capsule Networks for Text Classification [PDF]
arXiv, 2020 Many text classification applications require models with satisfying performance as well as good interpretability. Traditional machine learning methods are easy to interpret but have low accuracies. The development of deep learning models boosts the performance significantly. However, deep learning models are typically hard to interpret.
arxiv
Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME [PDF]
arXiv, 2022 Nowadays, deep neural networks are being used in many domains because of their high accuracy results. However, they are considered as "black box", means that they are not explainable for humans. On the other hand, in some tasks such as medical, economic, and self-driving cars, users want the model to be interpretable to decide if they can trust these ...
arxiv
Interpretable Representations in Explainable AI: From Theory to Practice [PDF]
, 2020 Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and machine learning algorithms. They translate the low-level data representation necessary for good predictive performance into high-level human-intelligible concepts used to convey the explanatory insights ...
arxiv +1 more source
Functional variation among LPMOs revealed by the inhibitory effects of cyanide and buffer ions
FEBS Letters, EarlyView.This study addresses the inhibition of lytic polysaccharide monooxygenases (LPMOs) by cyanide and explains how and why the magnitude of observed inhibitory effects depends on the way LPMO reactions are setup and on the type of LPMO. Enzymes known as lytic polysaccharide monooxygenases (LPMOs) are mono‐copper polysaccharide‐degrading peroxygenases that ...
Ole Golten +10 more
wiley +1 more source
Making All Children Count: Teach For All and the Universalizing Appeal of Data
Education Policy Analysis Archives, 2015 In this paper, we argue that in order to bind Teach For All’s universal/izing statement of problems and solutions to the specificities and the special conditions of member programs’ local contexts, what is needed is a shared set of discursive practices ...
Daniel Friedrich +2 more
doaj +1 more source
Multi-block Analysis of Genomic Data Using Generalized Canonical Correlation Analysis [PDF]
Genomics & Informatics, 2018 Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed to find genes associated with complex diseases.
Inyoung Jun, Wooree Choi, Mira Park
doaj +1 more source
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning [PDF]
arXiv, 2022 Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems ...
arxiv
Characteristics of the Kelch domain containing (KLHDC) subfamily and relationships with diseases
FEBS Letters, EarlyView.The Kelch protein superfamily includes 63 members, with the KLHDC subfamily having 10 proteins. While their functions are not fully understood, recent advances in KLHDC2's structure and role in protein degradation have highlighted its potential for drug development, especially in PROTAC therapies.
Courtney Pilcher +6 more
wiley +1 more source
Sensors, 2020 Seepage flow through embankment dams and their sub-base is a crucial safety concern that can initiate internal erosion of the structure. The thermometric method of seepage monitoring employs the study of heat transfer characteristics in the soils, as the
Yaser Ghafoori +3 more
doaj +1 more source
Interpretable time series neural representation for classification purposes [PDF]
arXiv, 2023 Deep learning has made significant advances in creating efficient representations of time series data by automatically identifying complex patterns. However, these approaches lack interpretability, as the time series is transformed into a latent vector that is not easily interpretable.
arxiv