From the 1 of 25 linked papers with an AI index.
14 papers · 1 filter
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…
Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search
Yashkumar R Lukhi, Harsh Rameshbhai Moradiya, Radu Timofte +1
We present an automated large-scale search pipeline for heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR neural network dataset ecosystem. Building o…
Towards Robust Training in NNGPT AutoML Pipeline: A Loss-Optimizer Pairing Selection Study
Anton Abramochkin, Radu Timofte, Dmitry Ignatov
The choice of loss function and optimizer is an important decision, that shapes further model training. Yet automated architecture search pipelines (AutoML) benefits significantly…