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cs.LG2026
Scaling Closed-Loop Feature Channel Configuration with LLMs
Tolgay Atinc Uzun, Radu Timofte, Dmitry Ignatov
Promising initial results in closed-loop large-language-model-based channel-configuration search demonstrated that neural-network widths can be optimized directly through executabl…
cs.LG2026
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…
cs.LG2025
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…