works on

From the 1 of 25 linked papers with an AI index.

collaborators
Showing cs.CVShow all

9 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs

Faraz Kayani, Sarmad Kayani, Asad Ahmed +2

While deep-learning-based image restoration has achieved unprecedented fidelity, deployment on mobile Neural Processing Units (NPUs) remains bottlenecked by operator incompatibilit…

cs.CV2026

Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models

Tolgay Atinc Uzun, Dmitry Ignatov, Radu Timofte

Channel-configuration search, the optimization of layer specifications such as channel widths in deep neural networks, presents a combinatorial challenge constrained by tensor-shap…

cs.CV2026

MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment

Arun Kumar, Aswathy Baiju, Radu Timofte +1

Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present MobileAgeNet, a lightweight…