mccormick.northwestern.edu

A Scalable Probabilistic Computer

Ising machines demonstrate significant potential to tackle computationally complex challenges, including combinatorial optimization problems related to logistics, manufacturing, finance, and AI. The ...
IEEE

Approximate and Stochastic Ising Machines

Abstract: The Ising model is useful in searching for (sub)-optimal solutions of combinatorial optimization problems (COPs). CMOS implementations of Ising model-based solvers, commonly referred to as ...
GitHub

ising-model-simulation

Add a description, image, and links to the ising-model-simulation topic page so that developers can more easily learn about it.
ascopubs.org

Time-dependent prediction model using modified DeepSurv algorithm for dynamic risk assessment of post-operative bone metastases in breast cancer.

Healthcare provider experiences in managing antibody-drug conjugate dosing adjustments due to nausea and vomiting: SABCS and ESMO survey findings. Effcacy and safety results of a multi-center phase ...
Scientific Research Publishing

Hybrid Software Model for Defect Detection and Cost Evaluation Using Support Vector Machine Algorithm ()

Software defect prediction and cost estimation are critical challenges in software engineering, directly influencing software quality and project management efficiency. This study presents a ...
C&EN

Efficient Model Predictive Control Implementation via Machine Learning: An Algorithm Selection and Configuration Approach

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
Healio

Large language model may improve diagnosis of multiple sclerosis

Please provide your email address to receive an email when new articles are posted on . The model predicted MS diagnosis with 91.5% specificity and non-MS diagnosis with 100% specificity. However, it ...
marktechpost

Meta AI Introduces MR.Q: A Model-Free Reinforcement Learning Algorithm with Model-Based Representations for Enhanced Generalization

Reinforcement learning (RL) trains agents to make sequential decisions by maximizing cumulative rewards. It has diverse applications, including robotics, gaming, and automation, where agents interact ...