Results 151 to 160 of about 259,517 (255)

ADMGCN: graph convolutional network for Alzheimer's disease diagnosis with a meta-learning paradigm. [PDF]

Bioinformatics
Sun X, Li J, Yan G, Han R.
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Few-shot cross-domain fault diagnosis via adversarial meta-learning. [PDF]

Sci Rep
Guo Y, Dai J, Zhang J.
europepmc   +1 more source

A learning theory of meta learning [PDF]

Natl Sci Rev
This paper gives a brief introduction to recent theoretical advance of meta learning.
europepmc   +3 more sources

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

A meta-learning ensemble framework for robust and interpretable prediction of emergency medical services demand. [PDF]

Sci Rep
Garg T, Toshniwal D, Parida M.
europepmc   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Structure-enhanced graph meta learning for few-shot gene regulatory network inference. [PDF]

Genome Biol
Yu W, Chen Z, Hu Y, Qin J, Ou-Yang L.
europepmc   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

A privacy-preserving federated meta-learning framework for cross-project defect prediction in software systems. [PDF]

Sci Rep
Potharlanka JL, Shaik KY, N BK.
europepmc   +1 more source