Activation functions in artificial neural networks: A systematic overview
J Lederer - arXiv preprint arXiv:2101.09957, 2021 - arxiv.org
Activation functions shape the outputs of artificial neurons and, therefore, are integral parts
of neural networks in general and deep learning in particular. Some activation functions …
of neural networks in general and deep learning in particular. Some activation functions …
Automated quality assessment of large digitised histology cohorts by artificial intelligence
M Haghighat, L Browning, K Sirinukunwattana… - Scientific Reports, 2022 - nature.com
Research using whole slide images (WSIs) of histopathology slides has increased
exponentially over recent years. Glass slides from retrospective cohorts, some with patient …
exponentially over recent years. Glass slides from retrospective cohorts, some with patient …
How do noise tails impact on deep ReLU networks?
How do noise tails impact on deep ReLU networks? Page 1 The Annals of Statistics 2024,
Vol. 52, No. 4, 1845–1871 https://doi.org/10.1214/24-AOS2428 © Institute of Mathematical …
Vol. 52, No. 4, 1845–1871 https://doi.org/10.1214/24-AOS2428 © Institute of Mathematical …
Statistical guarantees for regularized neural networks
Neural networks have become standard tools in the analysis of data, but they lack
comprehensive mathematical theories. For example, there are very few statistical …
comprehensive mathematical theories. For example, there are very few statistical …
Statistical guarantees for sparse deep learning
J Lederer - AStA Advances in Statistical Analysis, 2024 - Springer
Neural networks are becoming increasingly popular in applications, but our mathematical
understanding of their potential and limitations is still limited. In this paper, we further this …
understanding of their potential and limitations is still limited. In this paper, we further this …
Robust nonparametric regression with deep neural networks
In this paper, we study the properties of robust nonparametric estimation using deep neural
networks for regression models with heavy tailed error distributions. We establish the non …
networks for regression models with heavy tailed error distributions. We establish the non …
Robust deep learning from weakly dependent data
W Kengne, M Wade - arXiv preprint arXiv:2405.05081, 2024 - arxiv.org
Recent developments on deep learning established some theoretical properties of deep
neural networks estimators. However, most of the existing works on this topic are restricted …
neural networks estimators. However, most of the existing works on this topic are restricted …
Non-asymptotic guarantees for robust statistical learning under infinite variance assumption
There has been a surge of interest in developing robust estimators for models with heavy-
tailed and bounded variance data in statistics and machine learning, while few works …
tailed and bounded variance data in statistics and machine learning, while few works …
Deep regression learning with optimal loss function
X Wang, L Zhou, H Lin - Journal of the American Statistical …, 2024 - Taylor & Francis
In this paper, we develop a novel efficient and robust nonparametric regression estimator
under a framework of a feedforward neural network (FNN). There are several interesting …
under a framework of a feedforward neural network (FNN). There are several interesting …
PathProfiler: automated quality assessment of retrospective histopathology whole-slide image cohorts by artificial intelligence–a case study for prostate cancer …
M Haghighat, L Browning, K Sirinukunwattana… - medRxiv, 2021 - medrxiv.org
Research using whole slide images (WSIs) of scanned histopathology slides for the
development of artificial intelligence (AI) algorithms has increased exponentially over recent …
development of artificial intelligence (AI) algorithms has increased exponentially over recent …