The paper introduces a leakage‑free, block‑based method for Neural Architecture Search (NAS) that shares runs across subjects, thereby avoiding the quadratic cost of a fully nested Leave‑One‑Subject‑Out (LOSO) evaluation. Applied to the BioVid Heat Pain dataset, the approach raises mean accuracy from 82.79 % to 83.39 % and can cut model parameters by up to 99.2 %.
arXiv:2609.21457v1 Announce Type: new Abstract: Deep neural architectures are widely used for signal processing in automated pain assessment systems. However, architecture design has remained largely...
ONNX-Net introduces a universal representation for neural architectures using natural language descriptions, enabling instant performance prediction across diverse search spaces. The authors present ONNX-Bench, a benchmark of over 600k architecture–accuracy pairs compiled from open‑source NAS‑bench networks in ONNX format. Experiments demonstrate strong zero‑shot predictive performance with minimal pretraining, overcoming the limitations of cell‑based, graph‑encoded approaches.
AutoBCI is an agentic framework that uses a Designer Agent and a Forecaster Agent to discover and select EEG decoding architectures across diverse tasks such as emotion recognition, motor imagery, and sleep staging. The Designer Agent performs Pool‑Guided Architecture Discovery (PGAD) to generate and refine models, while the Forecaster Agent uses Performance Estimation from Early Knowledge (PEEK) to predict full‑budget validation performance from early learning curves. In experiments on 14 EEG datasets, AutoBCI with Claude Opus 5.5 achieved a 64.16% average test balanced accuracy, slightly surpassing the best baseline, and PEEK reduced prediction error by 38.1% compared to the best-observed-score baseline.
arXiv:2606. 07664v1 Announce Type: cross Abstract: Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms.
arXiv:2606. 23706v1 Announce Type: cross Abstract: The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective neural biomarkers in mental health.
Ensembles are a standard way to improve the performance and robustness of deep neural networks, but their effectiveness crucially depends on both the quality and the diversity of individual models. Most neural architecture search (NAS) methods are computationally expensive.
The paper investigates the use of Zero Cost Proxies (ZCPs) to identify high‑performing wearable Human Activity Recognition (HAR) models without full training. Eight ZCPs were evaluated across six benchmark HAR datasets, showing that the top‑predicted architectures achieve performance within 7% of fully trained models, and training the top‑10 predictions reaches within 2% of full training. This demonstrates that ZCPs can significantly reduce computational costs while maintaining competitive accuracy in sensor‑based HAR tasks.
arXiv:2608.28267v1 Announce Type: new Abstract: Randomized neural networks enable fast and analytically tractable training by fixing the input to hidden layer parameters at random and learning the ou...
arXiv:2607. 26940v1 Announce Type: new Abstract: Ensembles are a standard way to improve the performance and robustness of deep neural networks, but their effectiveness crucially depends on both the quality and the diversity of individual models.
arXiv:2608.21712v1 Announce Type: new Abstract: Transformer-based models are widely used for clinical prediction from electronic health records (EHRs), yet their architectures still require substanti...
arXiv:2608. 14472v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) aims to automate neural network architecture design, reducing reliance on human expertise.
The paper introduces PPNAS, a prediction‑powered inference method for neural architecture search that combines a small set of accurately evaluated architectures with a large set of zero‑cost proxy (ZCP) evaluations. PPNAS uses the ordinal information from ZCPs to generate pairwise ranking supervision and applies a debiasing step to reconcile discrepancies between proxy and true performance rankings. Experiments show that PPNAS achieves state‑of‑the‑art results in predictor‑based NAS under limited evaluation budgets.