1 citations · 1 across the 4 of their papers we have counts for
5 papers
APOLO: Automatic Prompt Optimization for Ontology Learning
Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu +3
Ontology Learning (OL) from text has advanced with the emergence of Large Language Models (LLMs), but it remains challenging due to the limited availability of annotated training d…
LEMUR 2: Unlocking Neural Network Diversity for AI
Tolgay Atinc Uzun, Waleed Khalid, Saif U Din +17
Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain or deployment-aware evaluatio…
NNGPT: Rethinking AutoML with Large Language Models
Roman Kochnev, Waleed Khalid, Tolgay Atinc Uzun +8
Building self-improving AI systems remains a fundamental challenge in the AI domain. We present NNGPT, an open-source framework that turns a large language model (LLM) into a self-…
LEMUR Neural Network Dataset: Towards Seamless AutoML
Arash Torabi Goodarzi, Roman Kochnev, Waleed Khalid +8
Neural networks are the backbone of modern artificial intelligence, but designing, evaluating, and comparing them remains labor-intensive. While numerous datasets exist for trainin…
Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?
Roman Kochnev, Arash Torabi Goodarzi, Zofia Antonina Bentyn +2
Optimal hyperparameter selection is critical for maximizing the performance of neural networks in computer vision, particularly as architectures become more complex. This work expl…