NAS
-
Analysis of Incursive Breast Cancer in Mammograms Using YOLO, Explainability, and Domain Adaptation
2025-11-28 08:48:05Jayan Adhikari, Prativa Joshi, Susish BaralAbstract
Deep learning models for breast cancer detection from mammographic images have significant reliability problems when presented with Out-of-Distribution (OOD) inputs such as other imaging modalities (CT, MRI, X-ray) or equipment variations, leading to unreliable detection and misdiagnosis. The current research mitigates the fundamental OOD issue through a comprehensive approach integrating ResNet50-based OOD filtering with YOLO architectures (YOLOv8, YOLOv11, YOLOv12) for accurate detection of breast cancer. Our strategy establishes an in-domain gallery via cosine similarity to rigidly reject non-mammographic inputs prior to processing, ensuring that only domain-associated images supply the detection pipeline. The OOD detection component achieves 99.77\% general accuracy with immaculate 100\% accuracy on OOD test sets, effectively eliminating irrelevant imaging modalities. ResNet50 was selected as the optimum backbone after 12 CNN architecture searches. The joint framework unites OOD robustness with high detection performance (mAP@0.5: 0.947) and enhanced interpretability through Grad-CAM visualizations. Experimental validation establishes that OOD filtering significantly improves system reliability by preventing false alarms on out-of-distribution inputs while maintaining higher detection accuracy on mammographic data. The present study offers a fundamental foundation for the deployment of reliable AI-based breast cancer detection systems in diverse clinical environments with inherent data heterogeneity.
Abstract (translated)
用于从乳腺X线摄影图像中检测乳腺癌的深度学习模型在面对与训练数据不同的“Out-of-Distribution”（OOD，非预期分布）输入时存在显著可靠性问题。这类OOD输入包括其他成像模式（如CT、MRI、X射线）或设备变化等，这些问题会导致检测不可靠和误诊。目前的研究通过一种综合性方法解决了这一根本的OOD问题，该方法结合了基于ResNet50的OOD过滤器与YOLO架构（包括YOLOv8、YOLOv11和YOLOv12），以实现乳腺癌的精确检测。 我们的策略通过余弦相似度建立了域内图像库，并严格拒绝非乳腺X线摄影输入，确保只有相关领域的图像才能进入检测流程。OOD检测组件实现了99.77%的一般准确率，在OOD测试集上达到了完美的100%准确率，有效地排除了其他成像模式的干扰。 经过12次CNN架构搜索后，选择了ResNet50作为最优骨干网络。该联合框架结合了OOD鲁棒性、高检测性能（mAP@0.5：0.947）以及通过Grad-CAM可视化增强的可解释性。实验验证表明，OOD过滤显著提高了系统的可靠性，减少了对非预期输入产生误报的情况，同时保持了乳腺X线摄影数据上的高检测准确性。 本研究为在存在数据异质性的多样临床环境中部署可靠的人工智能乳腺癌检测系统提供了基础框架。
URL
https://arxiv.org/abs/2512.00129
PDF
-
Neural Architecture Search for Quantum Autoencoders
2025-11-24 15:55:44Hibah Agha, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Shinjae YooAbstract
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in quantum machine learning (QML). Among QML techniques, quantum autoencoders show promise for compressing high-dimensional quantum and classical data. However, designing effective quantum circuit architectures for quantum autoencoders remains challenging due to the complexity of selecting gates, arranging circuit layers, and tuning parameters. This paper proposes a neural architecture search (NAS) framework that automates the design of quantum autoencoders using a genetic algorithm (GA). By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders for data reconstruction without becoming trapped in local minima. We demonstrate effectiveness on image datasets, highlighting the potential of quantum autoencoders for efficient feature extraction within a noise-prone, near-term quantum era. Our approach lays a foundation for broader application of genetic algorithms to quantum architecture search, aiming for a robust, automated method that can adapt to varied data and hardware constraints.
Abstract (translated)
URL
https://arxiv.org/abs/2511.19246
PDF
-
MFI-ResNet: Efficient ResNet Architecture Optimization via MeanFlow Compression and Selective Incubation
2025-11-16 02:33:50Nuolin Sun, Linyuan Wang, Haonan Wei, Lei Li, Bin YanAbstract
ResNet has achieved tremendous success in computer vision through its residual connection mechanism. ResNet can be viewed as a discretized form of ordinary differential equations (ODEs). From this perspective, the multiple residual blocks within a single ResNet stage essentially perform multi-step discrete iterations of the feature transformation for that stage. The recently proposed flow matching model, MeanFlow, enables one-step generative modeling by learning the mean velocity field to transform distributions. Inspired by this, we propose MeanFlow-Incubated ResNet (MFI-ResNet), which employs a compression-expansion strategy to jointly improve parameter efficiency and discriminative performance. In the compression phase, we simplify the multi-layer structure within each ResNet stage to one or two MeanFlow modules to construct a lightweight meta model. In the expansion phase, we apply a selective incubation strategy to the first three stages, expanding them to match the residual block configuration of the baseline ResNet model, while keeping the last stage in MeanFlow form, and fine-tune the incubated model. Experimental results show that on CIFAR-10 and CIFAR-100 datasets, MFI-ResNet achieves remarkable parameter efficiency, reducing parameters by 46.28% and 45.59% compared to ResNet-50, while still improving accuracy by 0.23% and 0.17%, respectively. This demonstrates that generative flow-fields can effectively characterize the feature transformation process in ResNet, providing a new perspective for understanding the relationship between generative modeling and discriminative learning.
Abstract (translated)
URL
https://arxiv.org/abs/2511.12422
PDF
-
Variational Quantum Algorithms for Particle Track Reconstruction
2025-11-14 15:24:59Vincenzo Lipardi, Xenofon Chiotopoulos, Jacco A. de Vries, Domenica Dibenedetto, Kurt Driessens, Marcel Merk, Mark H. M. WinandsAbstract
Quantum Computing is a rapidly developing field with the potential to tackle the increasing computational challenges faced in high-energy physics. In this work, we explore the potential and limitations of variational quantum algorithms in solving the particle track reconstruction problem. We present an analysis of two distinct formulations for identifying straight-line tracks in a multilayer detection system, inspired by the LHCb vertex detector. The first approach is formulated as a ground-state energy problem, while the second approach is formulated as a system of linear equations. This work addresses one of the main challenges when dealing with variational quantum algorithms on general problems, namely designing an expressive and efficient quantum ansatz working on tracking events with fixed detector geometry. For this purpose, we employed a quantum architecture search method based on Monte Carlo Tree Search to design the quantum circuits for different problem sizes. We provide experimental results to test our approach on both formulations for different problem sizes in terms of performance and computational cost.
Abstract (translated)
URL
https://arxiv.org/abs/2511.11397
PDF
-
RF-DETR: Neural Architecture Search for Real-Time Detection Transformers
2025-11-12 18:58:39Isaac Robinson, Peter Robicheaux, Matvei Popov, Deva Ramanan, Neehar PeriAbstract
Open-vocabulary detectors achieve impressive performance on COCO, but often fail to generalize to real-world datasets with out-of-distribution classes not typically found in their pre-training. Rather than simply fine-tuning a heavy-weight vision-language model (VLM) for new domains, we introduce RF-DETR, a light-weight specialist detection transformer that discovers accuracy-latency Pareto curves for any target dataset with weight-sharing neural architecture search (NAS). Our approach fine-tunes a pre-trained base network on a target dataset and evaluates thousands of network configurations with different accuracy-latency tradeoffs without re-training. Further, we revisit the "tunable knobs" for NAS to improve the transferability of DETRs to diverse target domains. Notably, RF-DETR significantly improves on prior state-of-the-art real-time methods on COCO and Roboflow100-VL. RF-DETR (nano) achieves 48.0 AP on COCO, beating D-FINE (nano) by 5.3 AP at similar latency, and RF-DETR (2x-large) outperforms GroundingDINO (tiny) by 1.2 AP on Roboflow100-VL while running 20x as fast. To the best of our knowledge, RF-DETR (2x-large) is the first real-time detector to surpass 60 AP on COCO. Our code is at this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2511.09554
PDF
-
Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training
2025-11-08 22:38:14Richard Goldman, Varun Komperla, Thomas Ploetz, Harish HaresamudramAbstract
A promising alternative to the computationally expensive Neural Architecture Search (NAS) involves the development of \textit{Zero Cost Proxies (ZCPs)}, which correlate well to trained performance, but can be computed through a single forward/backward pass on a randomly sampled batch of data. In this paper, we investigate the effectiveness of ZCPs for HAR on six benchmark datasets, and demonstrate that they discover network architectures that obtain within 5\% of performance attained by full scale training involving 1500 randomly sampled architectures. This results in substantial computational savings as high performing architectures can be discovered with minimal training. Our experiments not only introduce ZCPs to sensor-based HAR, but also demonstrate that they are robust to data noise, further showcasing their suitability for practical scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2511.06157
PDF
-
Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification
2025-11-04 20:52:56Mikhael Djajapermana, Moritz Reiber, Daniel Mueller-Gritschneder, Ulf SchlichtmannAbstract
Hybrids of Convolutional Neural Network (CNN) and Vision Transformer (ViT) have outperformed pure CNN or ViT architecture. However, since these architectures require large parameters and incur large computational costs, they are unsuitable for tinyML deployment. This paper introduces a new hybrid CNN-ViT search space for Neural Architecture Search (NAS) to find efficient hybrid architectures for image classification. The search space covers hybrid CNN and ViT blocks to learn local and global information, as well as the novel Pooling block of searchable pooling layers for efficient feature map reduction. Experimental results on the CIFAR10 dataset show that our proposed search space can produce hybrid CNN-ViT architectures with superior accuracy and inference speed to ResNet-based tinyML models under tight model size constraints.
Abstract (translated)
URL
https://arxiv.org/abs/2511.02992
PDF
-
RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets
2025-11-03 09:36:27Muhammed Yusuf Kartal (TOBB University of Economics and Technology), Suha Kagan Kose (Roketsan Inc), Korhan Sevin\c{c} (TOBB University of Economics and Technology), Burak Aktas (Roketsan Inc)Abstract
Retrieval-Augmented Generation (RAG) quality depends on many interacting choices across retrieval, ranking, augmentation, prompting, and generation, so optimizing modules in isolation is brittle. We introduce RAGSmith, a modular framework that treats RAG design as an end-to-end architecture search over nine technique families and 46{,}080 feasible pipeline configurations. A genetic search optimizes a scalar objective that jointly aggregates retrieval metrics (recall@k, mAP, nDCG, MRR) and generation metrics (LLM-Judge and semantic similarity). We evaluate on six Wikipedia-derived domains (Mathematics, Law, Finance, Medicine, Defense Industry, Computer Science), each with 100 questions spanning factual, interpretation, and long-answer types. RAGSmith finds configurations that consistently outperform naive RAG baseline by +3.8\% on average (range +1.2\% to +6.9\% across domains), with gains up to +12.5\% in retrieval and +7.5\% in generation. The search typically explores $\approx 0.2\%$ of the space ($\sim 100$ candidates) and discovers a robust backbone -- vector retrieval plus post-generation reflection/revision -- augmented by domain-dependent choices in expansion, reranking, augmentation, and prompt reordering; passage compression is never selected. Improvement magnitude correlates with question type, with larger gains on factual/long-answer mixes than interpretation-heavy sets. These results provide practical, domain-aware guidance for assembling effective RAG systems and demonstrate the utility of evolutionary search for full-pipeline optimization.
Abstract (translated)
URL
https://arxiv.org/abs/2511.01386
PDF
-
Elastic Architecture Search for Efficient Language Models
Abstract
As large pre-trained language models become increasingly critical to natural language understanding (NLU) tasks, their substantial computational and memory requirements have raised significant economic and environmental concerns. Addressing these challenges, this paper introduces the Elastic Language Model (ELM), a novel neural architecture search (NAS) method optimized for compact language models. ELM extends existing NAS approaches by introducing a flexible search space with efficient transformer blocks and dynamic modules for dimension and head number adjustment. These innovations enhance the efficiency and flexibility of the search process, which facilitates more thorough and effective exploration of model architectures. We also introduce novel knowledge distillation losses that preserve the unique characteristics of each block, in order to improve the discrimination between architectural choices during the search process. Experiments on masked language modeling and causal language modeling tasks demonstrate that models discovered by ELM significantly outperform existing methods.
Abstract (translated)
URL
https://arxiv.org/abs/2510.27037
PDF
-
A Survey on LLM Mid-training
2025-10-27 07:32:19Chengying Tu, Xuemiao Zhang, Rongxiang Weng, Rumei Li, Chen Zhang, Yang Bai, Hongfei Yan, Jingang Wang, Xunliang CaiAbstract
Recent advances in foundation models have highlighted the significant benefits of multi-stage training, with a particular emphasis on the emergence of mid-training as a vital stage that bridges pre-training and post-training. Mid-training is distinguished by its use of intermediate data and computational resources, systematically enhancing specified capabilities such as mathematics, coding, reasoning, and long-context extension, while maintaining foundational competencies. This survey provides a formal definition of mid-training for large language models (LLMs) and investigates optimization frameworks that encompass data curation, training strategies, and model architecture optimization. We analyze mainstream model implementations in the context of objective-driven interventions, illustrating how mid-training serves as a distinct and critical stage in the progressive development of LLM capabilities. By clarifying the unique contributions of mid-training, this survey offers a comprehensive taxonomy and actionable insights, supporting future research and innovation in the advancement of LLMs.
Abstract (translated)
URL
https://arxiv.org/abs/2510.23081
PDF
-
LoMix: Learnable Weighted Multi-Scale Logits Mixing for Medical Image Segmentation
2025-10-27 04:25:57Md Mostafijur Rahman, Radu MarculescuAbstract
U-shaped networks output logits at multiple spatial scales, each capturing a different blend of coarse context and fine detail. Yet, training still treats these logits in isolation - either supervising only the final, highest-resolution logits or applying deep supervision with identical loss weights at every scale - without exploring mixed-scale combinations. Consequently, the decoder output misses the complementary cues that arise only when coarse and fine predictions are fused. To address this issue, we introduce LoMix (Logits Mixing), a NAS-inspired, differentiable plug-and-play module that generates new mixed-scale outputs and learns how exactly each of them should guide the training process. More precisely, LoMix mixes the multi-scale decoder logits with four lightweight fusion operators: addition, multiplication, concatenation, and attention-based weighted fusion, yielding a rich set of synthetic mutant maps. Every original or mutant map is given a softplus loss weight that is co-optimized with network parameters, mimicking a one-step architecture search that automatically discovers the most useful scales, mixtures, and operators. Plugging LoMix into recent U-shaped architectures (i.e., PVT-V2-B2 backbone with EMCAD decoder) on Synapse 8-organ dataset improves DICE by +4.2% over single-output supervision, +2.2% over deep supervision, and +1.5% over equally weighted additive fusion, all with zero inference overhead. When training data are scarce (e.g., one or two labeled scans), the advantage grows to +9.23%, underscoring LoMix's data efficiency. Across four benchmarks and diverse U-shaped networks, LoMiX improves DICE by up to +13.5% over single-output supervision, confirming that learnable weighted mixed-scale fusion generalizes broadly while remaining data efficient, fully interpretable, and overhead-free at inference. Our code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2510.22995
PDF
-
SAND: A Self-supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection
2025-10-24 18:55:00Zhixin Pan, Ziyu Shu, Linh Nguyen, Amberbir AlemayohAbstract
The globalized semiconductor supply chain has made Hardware Trojans (HT) a significant security threat to embedded systems, necessitating the design of efficient and adaptable detection mechanisms. Despite promising machine learning-based HT detection techniques in the literature, they suffer from ad hoc feature selection and the lack of adaptivity, all of which hinder their effectiveness across diverse HT attacks. In this paper, we propose SAND, a selfsupervised and adaptive NAS-driven framework for efficient HT detection. Specifically, this paper makes three key contributions. (1) We leverage self-supervised learning (SSL) to enable automated feature extraction, eliminating the dependency on manually engineered features. (2) SAND integrates neural architecture search (NAS) to dynamically optimize the downstream classifier, allowing for seamless adaptation to unseen benchmarks with minimal fine-tuning. (3) Experimental results show that SAND achieves a significant improvement in detection accuracy (up to 18.3%) over state-of-the-art methods, exhibits high resilience against evasive Trojans, and demonstrates strong generalization.
Abstract (translated)
URL
https://arxiv.org/abs/2510.23643
PDF
-
Towards Low-Latency and Adaptive Ransomware Detection Using Contrastive Learning
2025-10-24 18:33:52Zhixin Pan, Ziyu Shu, Amberbir AlemayohAbstract
Ransomware has become a critical threat to cybersecurity due to its rapid evolution, the necessity for early detection, and growing diversity, posing significant challenges to traditional detection methods. While AI-based approaches had been proposed by prior works to assist ransomware detection, existing methods suffer from three major limitations, ad-hoc feature dependencies, delayed response, and limited adaptability to unseen variants. In this paper, we propose a framework that integrates self-supervised contrastive learning with neural architecture search (NAS) to address these challenges. Specifically, this paper offers three important contributions. (1) We design a contrastive learning framework that incorporates hardware performance counters (HPC) to analyze the runtime behavior of target ransomware. (2) We introduce a customized loss function that encourages early-stage detection of malicious activity, and significantly reduces the detection latency. (3) We deploy a neural architecture search (NAS) framework to automatically construct adaptive model architectures, allowing the detector to flexibly align with unseen ransomware variants. Experimental results show that our proposed method achieves significant improvements in both detection accuracy (up to 16.1%) and response time (up to 6x) compared to existing approaches while maintaining robustness under evasive attacks.
Abstract (translated)
URL
https://arxiv.org/abs/2510.21957
PDF
-
Comparative Analysis of Object Detection Algorithms for Surface Defect Detection
2025-10-21 10:05:02Arpan Maity, Tamal GhoshAbstract
This article compares the performance of six prominent object detection algorithms, YOLOv11, RetinaNet, Fast R-CNN, YOLOv8, RT-DETR, and DETR, on the NEU-DET surface defect detection dataset, comprising images representing various metal surface defects, a crucial application in industrial quality control. Each model's performance was assessed regarding detection accuracy, speed, and robustness across different defect types such as scratches, inclusions, and rolled-in scales. YOLOv11, a state-of-the-art real-time object detection algorithm, demonstrated superior performance compared to the other methods, achieving a remarkable 70% higher accuracy on average. This improvement can be attributed to YOLOv11s enhanced feature extraction capabilities and ability to process the entire image in a single forward pass, making it faster and more efficient in detecting minor surface defects. Additionally, YOLOv11's architecture optimizations, such as improved anchor box generation and deeper convolutional layers, contributed to more precise localization of defects. In conclusion, YOLOv11's outstanding performance in accuracy and speed solidifies its position as the most effective model for surface defect detection on the NEU dataset, surpassing competing algorithms by a substantial margin.
Abstract (translated)
URL
https://arxiv.org/abs/2510.21811
PDF
-
Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimization
Abstract
Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has recently been introduced to enable advanced control over electromagnetic waves to further increase the benefits of traditional RIS in enhancing signal quality and improving spectral and energy efficiency for next-generation wireless networks. A significant issue in designing and deploying BD-RIS is the tradeoff between its performance and circuit complexity. Despite some efforts in exploring optimal architectures with the lowest circuit complexities for ideal BD-RIS, architecture discovery for non-ideal BD-RIS remains uninvestigated. Therefore, how non-idealities and circuit complexity jointly affect the performance of BD-RIS remains unclear, making it difficult to achieve the performance - circuit complexity tradeoff in the presence of non-idealities. Essentially, architecture discovery for non-ideal BD-RIS faces challenges from both the computational complexity of global architecture search and the difficulty in achieving global optima. To tackle these challenges, we propose a learning-based two-tier architecture discovery framework (LTTADF) consisting of an architecture generator and a performance optimizer to jointly discover optimal architectures of non-ideal BD-RIS given specific circuit complexities, which can effectively explore over a large architecture space while avoiding getting trapped in poor local optima and thus achieving near-optimal solutions for the performance optimization. Numerical results provide valuable insights for deploying non-ideal BD-RIS considering the performance - circuit complexity tradeoff.
Abstract (translated)
超越对角线可重构智能表面（BD-RIS）最近被引入，以实现对电磁波的高级控制，从而进一步提升传统RIS在增强信号质量和提高频谱及能源效率方面的能力，为下一代无线网络提供更多的优势。设计和部署BD-RIS时面临的一个重要问题在于其性能与电路复杂度之间的权衡。尽管有一些研究致力于探索理想BD-RIS的最低电路复杂度架构，但对于非理想的BD-RIS架构发现的研究仍然未被开展。因此，在非理想条件下，BD-RIS性能如何受到非理想性和电路复杂度共同影响尚不清楚，这使得在存在非理想性的情况下实现性能与电路复杂度之间的权衡变得困难。 实际上，对于非理想的BD-RIS来说，架构发现面临着来自全局架构搜索计算复杂性的挑战以及达到全局最优解的难度。为了解决这些问题，我们提出了一种基于学习的双层架构发现框架（LTTADF），该框架由一个架构生成器和一个性能优化器组成，它们可以共同在给定特定电路复杂度的情况下，探索非理想BD-RIS的最佳架构，从而有效地避免陷入次优解并实现接近最优的解决方案。数值结果为考虑性能与电路复杂度权衡的非理想BD-RIS部署提供了有价值的见解。 此框架的目标是通过自动化和优化的方法来解决设计非理想BD-RIS时遇到的问题，使研究者能够更高效地探索不同架构的可能性，并找到在特定条件下的最佳平衡点。这将有助于推动无线通信技术的发展，并为未来的无线网络带来更大的灵活性和效率。
URL
https://arxiv.org/abs/2510.15701
PDF
-
SGM: A Statistical Godel Machine for Risk-Controlled Recursive Self-Modification
2025-10-11 14:09:37Xuening Wu, Shenqin Yin, Yanlan Kang, Xinhang Zhang, Qianya Xu, Zeping Chen, Wenqiang ZhangAbstract
Recursive self-modification is increasingly central in AutoML, neural architecture search, and adaptive optimization, yet no existing framework ensures that such changes are made safely. Godel machines offer a principled safeguard by requiring formal proofs of improvement before rewriting code; however, such proofs are unattainable in stochastic, high-dimensional settings. We introduce the Statistical Godel Machine (SGM), the first statistical safety layer for recursive edits. SGM replaces proof-based requirements with statistical confidence tests (e-values, Hoeffding bounds), admitting a modification only when superiority is certified at a chosen confidence level, while allocating a global error budget to bound cumulative risk across this http URL also propose Confirm-Triggered Harmonic Spending (CTHS), which indexes spending by confirmation events rather than rounds, concentrating the error budget on promising edits while preserving familywise this http URL across supervised learning, reinforcement learning, and black-box optimization validate this role: SGM certifies genuine gains on CIFAR-100, rejects spurious improvement on ImageNet-100, and demonstrates robustness on RL and optimization this http URL, these results position SGM as foundational infrastructure for continual, risk-aware self-modification in learning this http URL is available at: this https URL.
Abstract (translated)
递归自我修改在自动机器学习（AutoML）、神经架构搜索和自适应优化中变得越来越重要，但现有的框架并不能保证这些更改的安全性。哥德尔机通过要求必须提供改进的正式证明来重新编写代码的方式提供了原则性的安全保护；然而，在随机性和高维度设置下，这样的证明是无法实现的。我们引入了统计哥德尔机器（SGM），这是第一个用于递归编辑的统计安全保障层。SGM用统计置信度测试（e值、Hoeffding界）代替基于证明的要求，并且只有当在选定的信心水平上被认证为更优越时才允许修改，同时分配全局误差预算以限制在整个过程中的累计风险。 我们还提出了确认触发谐波花费（CTHS），它通过确认事件而不是轮次来索引支出，在保留家族级错误率的同时将误差预算集中在有希望的编辑上。在监督学习、强化学习和黑盒优化中验证了SGM的作用：SGM在CIFAR-100数据集上认证了真正的改进，拒绝了ImageNet-100上的虚假改善，并且在RL和优化任务上展示了鲁棒性。 这些结果将SGM定位为持续风险意识自我修改学习的基础架构。更多详细信息，请参阅：https://this https URL.（请注意，最后一个链接应指向实际的论文或资源位置，而非占位符）
URL
https://arxiv.org/abs/2510.10232
PDF
-
PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search
2025-10-10 04:22:14Xiaolong Tu, Dawei Chen, Kyungtae Han, Onur Altintas, Haoxin WangAbstract
Hardware-Aware Neural Architecture Search (HW-NAS) has emerged as a powerful tool for designing efficient deep neural networks (DNNs) tailored to edge devices. However, existing methods remain largely impractical for real-world deployment due to their high time cost, extensive manual profiling, and poor scalability across diverse hardware platforms with complex, device-specific energy behavior. In this paper, we present PlatformX, a fully automated and transferable HW-NAS framework designed to overcome these limitations. PlatformX integrates four key components: (i) an energy-driven search space that expands conventional NAS design by incorporating energy-critical configurations, enabling exploration of high-efficiency architectures; (ii) a transferable kernel-level energy predictor across devices and incrementally refined with minimal on-device samples; (iii) a Pareto-based multi-objective search algorithm that balances energy and accuracy to identify optimal trade-offs; and (iv) a high-resolution runtime energy profiling system that automates on-device power measurement using external monitors without human intervention. We evaluate PlatformX across multiple mobile platforms, showing that it significantly reduces search overhead while preserving accuracy and energy fidelity. It identifies models with up to 0.94 accuracy or as little as 0.16 mJ per inference, both outperforming MobileNet-V2 in accuracy and efficiency. Code and tutorials are available at this http URL.
Abstract (translated)
硬件感知神经架构搜索（HW-NAS）已成为设计适用于边缘设备的高效深度神经网络（DNNs）的强大工具。然而，现有的方法由于高昂的时间成本、大量的人工分析以及在具有复杂且特定于设备的能量行为的不同硬件平台上的可扩展性差等问题，在实际部署中仍然存在较大挑战。本文介绍了PlatformX，这是一种全自动且跨平台转移的HW-NAS框架，旨在克服上述限制。PlatformX整合了四个关键组成部分：(i) 一个以能量驱动的搜索空间，通过纳入临界能耗配置来拓展传统的NAS设计，从而探索高效率架构；(ii) 设备间可转移的内核级能耗预测器，可通过少量设备样本逐步优化；(iii) 基于帕累托前沿的多目标搜索算法，平衡能量与准确率以识别最优权衡点；以及(iv) 高分辨率运行时能耗分析系统，自动执行无须人工干预的设备功耗测量。我们在多个移动平台上对PlatformX进行了评估，结果显示它显著减少了搜索开销，并且在保持准确性的同时也维持了能源效用水平。所发现的模型能够达到高达0.94的准确率或低至0.16毫焦每推理能耗，在精度和效率方面均优于MobileNet-V2。代码和教程可在提供的URL上获取。
URL
https://arxiv.org/abs/2510.08993
PDF
-
Detecting spills using thermal imaging, pretrained deep learning models, and a robotic platform
2025-10-09 19:40:58Gregory Yeghiyan, Jurius Azar, Devson Butani, Chan-Jin ChungAbstract
This paper presents a real-time spill detection system that utilizes pretrained deep learning models with RGB and thermal imaging to classify spill vs. no-spill scenarios across varied environments. Using a balanced binary dataset (4,000 images), our experiments demonstrate the advantages of thermal imaging in inference speed, accuracy, and model size. We achieve up to 100% accuracy using lightweight models like VGG19 and NasNetMobile, with thermal models performing faster and more robustly across different lighting conditions. Our system runs on consumer-grade hardware (RTX 4080) and achieves inference times as low as 44 ms with model sizes under 350 MB, highlighting its deployability in safety-critical contexts. Results from experiments with a real robot and test datasets indicate that a VGG19 model trained on thermal imaging performs best.
Abstract (translated)
这篇论文介绍了一个实时漏油检测系统，该系统利用预训练的深度学习模型结合RGB和热成像技术来区分泄漏与非泄漏场景，并且可以在各种环境中应用。通过使用一个平衡的二元数据集（4,000张图像），我们的实验展示了热成像在推理速度、准确性以及模型大小方面的优势。我们能够达到高达100%的准确率，所使用的轻量级模型包括VGG19和NasNetMobile，其中基于热成像的模型在不同的光照条件下表现出更快的速度和更强的鲁棒性。 我们的系统可以在消费级硬件（如RTX 4080）上运行，并且可以达到低至44毫秒的推理时间以及低于350MB的模型大小，这突显了其在安全关键环境中的部署能力。通过与实际机器人及测试数据集进行实验的结果表明，基于热成像训练的VGG19模型表现最佳。
URL
https://arxiv.org/abs/2510.08770
PDF
-
Universal Neural Architecture Space: Covering ConvNets, Transformers and Everything in Between
2025-10-07 15:31:40Ond\v{r}ej T\'ybl, Luk\'a\v{s} NeumannAbstract
We introduce Universal Neural Architecture Space (UniNAS), a generic search space for neural architecture search (NAS) which unifies convolutional networks, transformers, and their hybrid architectures under a single, flexible framework. Our approach enables discovery of novel architectures as well as analyzing existing architectures in a common framework. We also propose a new search algorithm that allows traversing the proposed search space, and demonstrate that the space contains interesting architectures, which, when using identical training setup, outperform state-of-the-art hand-crafted architectures. Finally, a unified toolkit including a standardized training and evaluation protocol is introduced to foster reproducibility and enable fair comparison in NAS research. Overall, this work opens a pathway towards systematically exploring the full spectrum of neural architectures with a unified graph-based NAS perspective.
Abstract (translated)
我们介绍了一种通用神经网络架构空间（UniNAS），这是一种用于神经架构搜索（NAS）的通用搜索空间，它将卷积网络、变压器及其混合架构统一在一个单一且灵活的框架下。我们的方法能够发现新型架构并使用同一框架分析现有的架构。此外，我们还提出了一种新的搜索算法，该算法允许在提议的搜索空间中进行遍历，并展示了此空间包含一些有趣的架构，在相同的训练设置下，这些架构的表现优于当前最先进的手工设计架构。最后，介绍了一个统一工具包，包括标准化的训练和评估协议，以促进研究结果的可重复性并支持NAS研究中的公平比较。总的来说，这项工作开辟了一条系统探索基于统一图论视角下的神经网络架构全谱系的道路。
URL
https://arxiv.org/abs/2510.06035
PDF
-
BioAutoML-NAS: An End-to-End AutoML Framework for Multimodal Insect Classification via Neural Architecture Search on Large-Scale Biodiversity Data
2025-10-07 13:00:12Arefin Ittesafun Abian, Debopom Sutradhar, Md Rafi Ur Rashid, Reem E. Mohamed, Md Rafiqul Islam, Asif Karim, Kheng Cher Yeo, Sami AzamAbstract
Insect classification is important for agricultural management and ecological research, as it directly affects crop health and production. However, this task remains challenging due to the complex characteristics of insects, class imbalance, and large-scale datasets. To address these issues, we propose BioAutoML-NAS, the first BioAutoML model using multimodal data, including images, and metadata, which applies neural architecture search (NAS) for images to automatically learn the best operations for each connection within each cell. Multiple cells are stacked to form the full network, each extracting detailed image feature representations. A multimodal fusion module combines image embeddings with metadata, allowing the model to use both visual and categorical biological information to classify insects. An alternating bi-level optimization training strategy jointly updates network weights and architecture parameters, while zero operations remove less important connections, producing sparse, efficient, and high-performing architectures. Extensive evaluation on the BIOSCAN-5M dataset demonstrates that BioAutoML-NAS achieves 96.81% accuracy, 97.46% precision, 96.81% recall, and a 97.05% F1 score, outperforming state-of-the-art transfer learning, transformer, AutoML, and NAS methods by approximately 16%, 10%, and 8% respectively. Further validation on the Insects-1M dataset obtains 93.25% accuracy, 93.71% precision, 92.74% recall, and a 93.22% F1 score. These results demonstrate that BioAutoML-NAS provides accurate, confident insect classification that supports modern sustainable farming.
Abstract (translated)
昆虫分类对于农业管理和生态研究至关重要，因为它直接影响作物的健康和产量。然而，由于昆虫具有复杂的特征、类不平衡以及大规模的数据集等问题，这一任务仍然极具挑战性。为了解决这些问题，我们提出了BioAutoML-NAS，这是第一个使用多模态数据（包括图像和元数据）的BioAutoML模型，并应用神经架构搜索（NAS）对图像进行自动学习，以确定每个连接中的最佳操作。多个细胞被堆叠起来形成完整的网络，每个细胞提取详细的图像特征表示。一个多模态融合模块结合了图像嵌入与元数据，使模型能够利用视觉和分类生物信息来分类昆虫。一种交替的双层优化训练策略共同更新网络权重和架构参数，并通过去除不重要的连接以生成稀疏、高效且性能高的架构。 在BIOSCAN-5M数据集上的广泛评估表明，BioAutoML-NAS实现了96.81%的准确率、97.46%的精确度、96.81%的召回率和97.05%的F1值，分别比最先进的迁移学习、Transformer模型、AutoML方法及NAS方法高出约16%、10%和8%。在Insects-1M数据集上的进一步验证获得了93.25%的准确率、93.71%的精确度、92.74%的召回率以及93.22%的F1值。 这些结果表明，BioAutoML-NAS能够提供准确且可靠的昆虫分类，支持现代可持续农业。
URL
https://arxiv.org/abs/2510.05888
PDF
