activity
20192026
most citedSurrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks

21 citations · 25 across the 12 of their papers we have counts for

collaborators

14 papers

quant-ph2026

Layered Quantum Architecture Search for 3D Point Cloud Classification

Natacha Kuete Meli, Jovita Lukasik, Vladislav Golyanik +1

We introduce layered Quantum Architecture Search (layered-QAS), a strategy inspired by classical network morphism that designs Parametrised Quantum Circuit (PQC) architectures by p…

cs.LG2025

ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architectures

Shiwen Qin, Alexander Auras, Shay B. Cohen +4

Neural architecture search (NAS) automates the design process of high-performing architectures, but remains bottlenecked by expensive performance evaluation. Most existing studies…

cs.LG2025

Smooth Model Compression without Fine-Tuning

Christina Runkel, Natacha Kuete Meli, Jovita Lukasik +3

Compressing and pruning large machine learning models has become a critical step towards their deployment in real-world applications. Standard pruning and compression techniques ar…

cs.LG2025

Transferrable Surrogates in Expressive Neural Architecture Search Spaces

Shiwen Qin, Gabriela Kadlecová, Martin Pilát +5

Neural architecture search (NAS) faces a challenge in balancing the exploration of expressive, broad search spaces that enable architectural innovation with the need for efficient…

cs.LG2024★ 2 cited

Surprisingly Strong Performance Prediction with Neural Graph Features

Gabriela Kadlecová, Jovita Lukasik, Martin Pilát +4

Performance prediction has been a key part of the neural architecture search (NAS) process, allowing to speed up NAS algorithms by avoiding resource-consuming network training. Alt…

cs.CV2024

Can We Talk Models Into Seeing the World Differently?

Paul Gavrikov, Jovita Lukasik, Steffen Jung +4

Unlike traditional vision-only models, vision language models (VLMs) offer an intuitive way to access visual content through language prompting by combining a large language model…