BMJ

Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and ...

2 State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, China 3 Chongqing Key Laboratory of Hematology and Microenvironment, Chongqing, China Participants This study analysed 471 newly ...
Frontiers

Machine learning model based on routine blood and biochemical parameters for early diagnosis of diabetic kidney disease

This retrospective study analyzed 3,114 diabetic patients from the Second Affiliated Hospital of Wannan Medical College (EDN1) and 1,496 patients from NHANES 2005-2018 (EDN2) for external validation.
Oak Ridge National Lab

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...
VentureBeat

MiniMax-M1 is a new open source model with 1 MILLION TOKEN context and new, hyper efficient reinforcement learning

Chinese AI startup MiniMax, perhaps best known in the West for its hit realistic AI video model Hailuo, has released its latest large language model, MiniMax-M1 — and in great news for enterprises and ...
VentureBeat

Ocient scales hyperscale data warehouse for machine learning

There's big data, then there is really big data, where there can be trillions of rows of data. That's the space where Chicago-based Ocient is in with its hyperscale data warehouse technology. Today ...
IEEE

Improvement of Classification Accuracy in Machine Learning Algorithm by Hyper-Parameter Optimization

Abstract: The manual optimization of hyperparameters is a straightforward and well-known approach, but it is not scalable, particularly when there are several settings and options. In nearly every ...
IEEE

The Effect of Hyper-parameters in Model-contrastive Federated Learning Algorithm

Abstract: Federated learning enables all parties to perform data analysis and machine learning through the updated use of global models. The key difficulty in federation learning is data heterogeneity ...