Add to ORKGAdvances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Colloquium provides a snapshot of nuclear physics research, which has been transformed by machine learning techniques
The use of machine learning is increasing at the LHC experiments including both the ATLAS and LHCb c...
This book provides a complete overview of the role of machine learning in radiation oncology and me...
The main focus of this work is to use machine learning and data mining techniques to address some ch...
Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses o...
Machine learning is an important applied research area in particle physics, beginning with applicati...
Our knowledge of the fundamental particles of nature and their interactions is summarized by the sta...
International audienceMachine learning (ML) encompasses a broad range of algorithms and modeling too...
Machine learning is an important applied research area in particle physics, beginning with applicati...
The use of computational algorithms, implemented on a computer, to extract information from data has...
Compelling experimental evidence suggests the existence of new physics beyond the well-established a...
In this talk, I will discuss machine learning tasks used in high energy physics. I will talk about s...
International audienceA recent burst of activity in applying machine learning to tackle fundamental ...
Advances in machine learning methods provide tools that have broad applicability in scientific resea...
Abstract: Machine learning, which builds on ideas in computer science, statistics, and optimization...
Charged particle tracking represents the largest consumer of CPU resources in high data volume Nucle...
The use of machine learning is increasing at the LHC experiments including both the ATLAS and LHCb c...
This book provides a complete overview of the role of machine learning in radiation oncology and me...
The main focus of this work is to use machine learning and data mining techniques to address some ch...
Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses o...
Machine learning is an important applied research area in particle physics, beginning with applicati...
Our knowledge of the fundamental particles of nature and their interactions is summarized by the sta...
International audienceMachine learning (ML) encompasses a broad range of algorithms and modeling too...
Machine learning is an important applied research area in particle physics, beginning with applicati...
The use of computational algorithms, implemented on a computer, to extract information from data has...
Compelling experimental evidence suggests the existence of new physics beyond the well-established a...
In this talk, I will discuss machine learning tasks used in high energy physics. I will talk about s...
International audienceA recent burst of activity in applying machine learning to tackle fundamental ...
Advances in machine learning methods provide tools that have broad applicability in scientific resea...
Abstract: Machine learning, which builds on ideas in computer science, statistics, and optimization...
Charged particle tracking represents the largest consumer of CPU resources in high data volume Nucle...
The use of machine learning is increasing at the LHC experiments including both the ATLAS and LHCb c...
This book provides a complete overview of the role of machine learning in radiation oncology and me...
The main focus of this work is to use machine learning and data mining techniques to address some ch...