From the 1 of 25 linked papers with an AI index.
25 papers
Device-First Feedback: Toward Mobile-Native LLM-Driven Neural Architecture Search
Saif U Din, Muhammad Ahsan Hussain, Radu Timofte +1
Deploying convolutional neural networks generated by large language models (LLMs) on real mobile hardware requires more than GPU validation accuracy: INT8 TensorFlow Lite export, d…
Similarity-Guided Curriculum Fine-Tuning of LLMs for Neural Architecture Synthesis
Anujaya Vijayakumar, Radu Timofte, Dmitry Ignatov
The paper proposes a MinHash‑based curriculum that gradually presents neural‑architecture code of increasing diversity to a large language model, fine‑tuning it with LoRA adapters…
Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
Hafsa Mateen, Radu Timofte, Dmitry Ignatov
Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While classical AutoML approaches often t…
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…
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…
LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation
Kabir Dev Paul Baghel, Radu Timofte, Dmitry Ignatov
Large language models (LLMs) can generate neural-network modifications, but unrestricted generation is often invalid or harmful. This paper studies a narrower setting: improving a…