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Found 6 papers, 3 papers with code
Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
In this way, the current work is designed to provide the plant phenotyping community with (i) methods for fast and accurate image-based feature extraction that require minimal training data, and (ii) a new population-scale data set, including 68 different leaf phenotypes, for domain scientists and machine learning researchers.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Varied phenomenology of models displaying dynamical large-deviation singularities
Singularities of dynamical large-deviation functions are often interpreted as the signal of a dynamical phase transition and the coexistence of distinct dynamical phases, by analogy with the correspondence between singularities of free energies and equilibrium phase behavior.
Statistical Mechanics
Evolutionary reinforcement learning of dynamical large deviations
We show how to calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning.
Parallel Accelerated Vector Similarity Calculations for Genomics Applications
The surge in availability of genomic data holds promise for enabling determination of genetic causes of observed individual traits, with applications to problems such as discovery of the genetic roots of phenotypes, be they molecular phenotypes such as gene expression or metabolite concentrations, or complex phenotypes such as diseases.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Performance 65Y05, 68W10
Parallel Accelerated Custom Correlation Coefficient Calculations for Genomics Applications
The massive quantities of genomic data being made available through gene sequencing techniques are enabling breakthroughs in genomic science in many areas such as medical advances in the diagnosis and treatment of diseases.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Performance 65Y05 [Computer aspects of numerical algorithms: Parallel computation], 68W10 [Algorithms: Parallel algorithms]
