[HTML][HTML] Explaining deep neural networks: A survey on the global interpretation methods
A substantial amount of research has been carried out in Explainable Artificial Intelligence
(XAI) models, especially in those which explain the deep architectures of neural networks. A …
(XAI) models, especially in those which explain the deep architectures of neural networks. A …
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …
and trusting statistical and deep learning models, as well as interpreting their predictions …
Relating the partial dependence plot and permutation feature importance to the data generating process
Scientists and practitioners increasingly rely on machine learning to model data and draw
conclusions. Compared to statistical modeling approaches, machine learning makes fewer …
conclusions. Compared to statistical modeling approaches, machine learning makes fewer …
Automl in the age of large language models: Current challenges, future opportunities and risks
The fields of both Natural Language Processing (NLP) and Automated Machine Learning
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …
Probabilistic machine learning for predicting desiccation cracks in clayey soils
With frequent heatwaves and drought-downpour cycles, climate change gives rise to severe
desiccation cracks. In this research, a probabilistic machine learning (ML) framework is …
desiccation cracks. In this research, a probabilistic machine learning (ML) framework is …
Can fairness be automated? Guidelines and opportunities for fairness-aware AutoML
The field of automated machine learning (AutoML) introduces techniques that automate
parts of the development of machine learning (ML) systems, accelerating the process and …
parts of the development of machine learning (ML) systems, accelerating the process and …
Efficient Hyperparameter Optimization with Adaptive Fidelity Identification
Abstract Hyperparameter Optimization and Neural Architecture Search are powerful in
attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing …
attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing …
Prediction of the resource consumption of distributed deep learning systems
The prediction of the resource consumption for the distributed training of deep learning
models is of paramount importance, as it can inform a priori users how long their training …
models is of paramount importance, as it can inform a priori users how long their training …
PED-ANOVA: efficiently quantifying hyperparameter importance in arbitrary subspaces
The recent rise in popularity of Hyperparameter Optimization (HPO) for deep learning has
highlighted the role that good hyperparameter (HP) space design can play in training strong …
highlighted the role that good hyperparameter (HP) space design can play in training strong …
A visual analytics conceptual framework for explorable and steerable partial dependence analysis
Machine learning techniques are a driving force for research in various fields, from credit
card fraud detection to stock analysis. Recently, a growing interest in increasing human …
card fraud detection to stock analysis. Recently, a growing interest in increasing human …