news.ucsb.edu

Proving the value of an old algorithm in training deep-learning models for AI

For about a decade, computer engineer Kerem Çamsari employed a novel approach known as probabilistic computing. Based on probabilistic bits (p-bits), it’s used to solve an array of complex ...
insideHPC

Distributed GPU Performance for Deep Learning Training

HPE highlights recent research that explores the performance of GPUs in scale-out and scale-up scenarios for deep learning training. As companies begin to move deep learning projects from the ...
University of Texas at San Antonio

RCSG TRAINING

The Research Computing Support Group (RCSG) at UT San Antonio offers specialized training sessions to support researchers with their computational needs. These training sessions cover high-performance ...
ascopubs.org

Optimizing chemotherapy decisions in low-resource settings with deep learning: A novel approach for unresectable NSCLC.

Validating a semantic similarity approach for automated data extraction in phase II oncology trials. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not ...
InfoWorld

MXNet review: Amazon’s scalable deep learning

Deep learning, which is basically neural network machine learning with multiple hidden layers, is all the rage—both for problems that justify the complexity and high computational cost of deep ...
ascopubs.org

Development and validation of a deep learning clinical decision support system to optimize inpatient mortality and palliative care referrals in cancer patients.

The wasting shadow: Underweight/cachexia as a consistent predictor of adverse inpatient outcomes and escalated resource use across common solid tumors.