Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

Researchers have developed a hybrid surrogate model for iso-octanol oxidation to iso-octanal that integrates data-driven ...

Researchers developed and validated a new lung cancer prediction model, Sybil-Epi, by integrating clinical and epidemiologic data with a pre-existing model.

The ModelSpy attack system reconstructs deep learning architectures from GPU electromagnetic emissions at up to six meters, ...

Development and Validation of an Artificial Intelligence Digital Pathology Biomarker to Predict Benefit of Long-Term Hormonal Therapy and Radiotherapy in Men With High-Risk Prostate Cancer Across ...

Impact of treatment patterns on clinical outcomes in patients of advanced pancreatic cancer treated with chemotherapy: A large-scale data analysis from real world practice. This is an ASCO Meeting ...

Researchers at EPFL have developed a deep-learning framework that dramatically improves vehicle re-identification in ...

Built on a new architecture KumoRFM-2 achieves state-of-the-art results across 41 predictive tasks and four major benchmarks, ...

New AGI lab aims to revolutionize machine learning with symbolic models, moving beyond traditional deep learning.

Over the past decades, computer scientists have introduced numerous artificial intelligence (AI) systems designed to emulate the organization and functioning of networks of neurons in the brain.

The deep learning model developed by researchers at the University of Pennsylvania identified severe heart dysfunction far more effectively than traditional AI methods. It also diagnosed 39 cardiac ...