Results 11 to 20 of about 851,309 (264)
Machine learning renormalization group for statistical physics
Machine Learning: Science and Technology, 2023 Abstract We develop a machine-learning renormalization group (MLRG) algorithm to explore and analyze many-body lattice models in statistical physics. Using the representation learning capability of generative modeling, MLRG automatically learns the optimal renormalization group (RG) transformations from self-generated spin configurations
Wanda Hou, Yi-Zhuang You
openaire +3 more sources
Classifier selection with permutation tests [PDF]
, 2017 This work presents a content-based recommender system for machine learning classifier algorithms. Given a new data set, a recommendation of what classifier is likely to perform best is made based on classifier performance over similar known data sets ...
Arias Vicente, Marta +2 more
core +1 more source
Anomaly detection for machine learning redshifts applied to SDSS galaxies
, 2015 We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates.
Bonnett, Christopher +5 more
core +1 more source
Application of statistical machine learning in biomarker selection
Scientific Reports, 2023 AbstractIn the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly prolonged overall survival relative to best supportive care alone as first-line maintenance therapy following first-line platinum-based chemotherapy in patients with advanced urothelial cancer (aUC).
Ritwik Vashistha +4 more
openaire +3 more sources
Learning the Structure for Structured Sparsity [PDF]
, 2015 Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings.
Bach, Francis, Shervashidze, Nino
core +5 more sources
Kernel methods in machine learning
, 2008 We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel.
Hofmann, Thomas +2 more
core +2 more sources
Normalized Information Distance [PDF]
, 2008 The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it.
Balbach, Frank J. +3 more
core +4 more sources
Single‐cell insights into the role of T cells in B‐cell malignancies
FEBS Letters, EarlyView.Single‐cell technologies have transformed our understanding of T cell–tumor cell interactions in B‐cell malignancies, revealing new T‐cell subsets, functional states, and immune evasion mechanisms. This Review synthesizes these findings, highlighting the roles of T cells in pathogenesis, progression, and therapy response, and underscoring their ...
Laura Llaó‐Cid
wiley +1 more source
From omics to AI—mapping the pathogenic pathways in type 2 diabetes
FEBS Letters, EarlyView.Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan +2 more
wiley +1 more source
Non-linear regression models for Approximate Bayesian Computation
, 2009 Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable.
A. Butler +43 more
core +1 more source