GitHub

Can Large Language Models Predict Parallel Code Performance?

We took this version of HeCBench and are modifying it to build the CUDA and OMP codes to gather their roofline performance data. So far we have a large portion of the CUDA and OMP codes building ...
Opinion
Business Daily Africa on MSNOpinion

KRA should harness AI to unlock Kenya’s customs potential

Global trade is evolving at an unprecedented pace, presenting customs authorities with the dual challenge of facilitating trade while safeguarding security and ensuring optimal revenue ...
Computer Weekly

Tricentis: Why AI writes code like a teenager (and why testers need to be adults)

If AI writes code like a teenager, then testers need to be the adults in the room. That doesn’t mean standing at the end of ...
InfoWorld

16 open source projects transforming AI and machine learning

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...
GitHub

CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding

CGBridge is a novel framework designed to enhance the code understanding capabilities of Large Language Models (LLMs) by integrating rich structural information from code graphs. Our approach follows ...
IEEE

Toward Low-Resource Languages Machine Translation: A Language-Specific Fine-Tuning With LoRA for Specialized Large Language Models

Abstract: In the field of computational linguistics, addressing machine translation (MT) challenges for low-resource languages remains crucial, as these languages often lack extensive data compared to ...
IEEE

LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs

Abstract: This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and ...