Add to ORKGThe application of machine learning in sciences has seen exciting advances in recent years. As a widely applicable technique, anomaly detection has been long studied in the machine learning community. Especially, deep neural nets-based out-of-distribution detection has made great progress for high-dimensional data. Recently, these techniques have been showing their potential in scientific disciplines. We take a critical look at their applicative prospects including data universality, experimental protocols, model robustness, etc. We discuss examples that display transferable practices and domain-specific challenges simultaneously, providing a starting point for establishing a novel interdisciplinary research paradigm in the near future.Comm...
145 pagesPropelled by large datasets and parallel compute accelerators, deep neural networks have re...
Neuroscientists are generating data sets of enormous size, which are matching the complexity of real...
Machine learning is increasingly becoming a ubiquitous discipline, because there are a lot of applic...
There has been a lot of recent interest in adopting machine learning methods for scientific and engi...
Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected ...
Progress in the sciences depends critically on the analysis of ever-growing bodies of data. Many of ...
While the introduction of practical deep learning has driven progress across scientific fields, rece...
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. The...
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force...
Although the debate of what data science is has a long history and has not reached a complete consen...
Off-the-shelf ML libraries combined with accessible scientific computing infrastructures continue to...
Deep neural networks have achieved state-of-the-art performance across a wide range of tasks. Convol...
“Deep learning” uses Post-Selection—selection of a model after training multiple models using data. ...
Technology drives advances in science. Giving scientists access to more powerful tools for collectin...
The future of bioimage analysis is increasingly defined by the development and use of tools that rel...
145 pagesPropelled by large datasets and parallel compute accelerators, deep neural networks have re...
Neuroscientists are generating data sets of enormous size, which are matching the complexity of real...
Machine learning is increasingly becoming a ubiquitous discipline, because there are a lot of applic...
There has been a lot of recent interest in adopting machine learning methods for scientific and engi...
Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected ...
Progress in the sciences depends critically on the analysis of ever-growing bodies of data. Many of ...
While the introduction of practical deep learning has driven progress across scientific fields, rece...
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. The...
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force...
Although the debate of what data science is has a long history and has not reached a complete consen...
Off-the-shelf ML libraries combined with accessible scientific computing infrastructures continue to...
Deep neural networks have achieved state-of-the-art performance across a wide range of tasks. Convol...
“Deep learning” uses Post-Selection—selection of a model after training multiple models using data. ...
Technology drives advances in science. Giving scientists access to more powerful tools for collectin...
The future of bioimage analysis is increasingly defined by the development and use of tools that rel...
145 pagesPropelled by large datasets and parallel compute accelerators, deep neural networks have re...
Neuroscientists are generating data sets of enormous size, which are matching the complexity of real...
Machine learning is increasingly becoming a ubiquitous discipline, because there are a lot of applic...