5 papers
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…
A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks
Waleed Khalid, Dmitry Ignatov, Radu Timofte
Reusing existing neural-network components is central to research efficiency, yet discovering, extracting, and validating such modules across thousands of open-source repositories…
From Memorization to Creativity: LLM as a Designer of Novel Neural Architectures
Waleed Khalid, Dmitry Ignatov, Radu Timofte
Large language models (LLMs) excel in program synthesis, yet their capacity for neural architecture design -- balancing syntactic reliability, performance, and structural novelty -…
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…