This work develops a general bilevel optimization framework for NAS across diverse architectures, including MLPs, CNNs, RNNs, and Transformers, to identify compact architectures with strong predictive performance.
Abstract
Neural Architecture Search (NAS) is naturally formulated as a bilevel optimization problem, where the upper-level optimizes the architecture using validation performance and the lower-level trains network parameters using training loss. However, NAS is computationally expensive due to discrete architectural decisions, exponentially growing search spaces, and the high cost of training candidate architectures. This work develops a general bilevel optimization framework for NAS across diverse architectures, including MLPs, CNNs, RNNs, and Transformers, to identify compact architectures with strong predictive performance. We propose three scalable formulations that replace discrete neuron- and activation-level decisions with continuous relaxations, enabling differentiable optimization over otherwise combinatorial architecture spaces. These formulations give rise to three NAS methods: NAS based on Neuron Gating (NAS-NG), NAS based on Mixed Activation (NAS-MA), and NAS based on Neuron Gating and Mixed Activation (NAS-NGMA). Experiments on MLPs and CNNs using MNIST and CIFAR-10 show that the proposed methods consistently identify compact architectures with competitive or improved predictive performance. On MNIST, NAS-NGMA achieves 98.68% test accuracy with 7.69M MLP parameters, while NAS-NG achieves 99.63% accuracy with only 0.26M CNN parameters. On CIFAR-10, the proposed methods consistently outperform vanilla DARTS. Further experiments demonstrate that NAS-NG can optimize substantially over-parameterized and literature-optimal architectures, improving accuracy while reducing parameters. These results establish relaxed bilevel optimization as a scalable alternative to discrete NAS and provide a general framework for efficient neuron- and activation-level architecture optimization.
Neural Architecture Search (NAS) aims to automate neural network architecture design, reducing reliance on human expertise. Among the various NAS methods, differentiable NAS has gained prominence due to its efficiency and accuracy compared to conventional NAS approaches. Since differentiable NAS relaxes the architecture search space into a continuous domain, it is possible to apply principles from continuous optimization to NAS. In this paper, we propose Linear Programming-based NAS (LP-NAS), a mathematical programming-based framework for differentiable NAS that is applicable to a wide range of continuous search spaces. LP-NAS formulates a linear program (LP) using the validation-loss gradient and the training-loss Hessian to compute an architecture update direction that improves generalization while preserving the optimality of the model parameters. By following this LP-derived descent direction, LP-NAS efficiently navigates the architecture search space, leading to faster and more effective architecture optimization. We introduce two computationally efficient variants of LP-NAS, namely S-LP-NAS and R-LP-NAS. Applying LP-NAS to the Differentiable Architecture Search (DARTS) search space results in two algorithmic variants, S-LP-DARTS and R-LP-DARTS. Both variants achieve faster convergence and significantly higher validation performance during the early search iterations than the standard DARTS algorithm. Extensive experiments on CIFAR-10 and CIFAR-100 show that LP-DARTS outperforms standard DARTS in both the architecture search and evaluation phases. Additionally, we compare our approach with several DARTS variants (P-DARTS, PC-DARTS, and STO-DARTS) on the CIFAR-10 dataset and demonstrate its effectiveness. Furthermore, we validate the transferability of the discovered architectures through experiments on the ImageNet dataset.
Deep neural networks often contain substantial parameter redundancy, resulting in unnecessary computational cost and energy consumption. This work presents a dynamically adaptive gating mechanism for learning layer-wise sparsity through differentiable masking. In the proposed framework, each network parameter is associated with a self-learning gate that controls its contribution during training. The gating function follows a progressive soft-to-hard transition in which the slope is gradually annealed, enabling the model to move smoothly from continuous parameter weighting to near-binary pruning decisions while simultaneously learning an adaptive threshold parameter. The framework is evaluated across multilayer perceptrons (MLPs), deep neural networks (DNNs), Tabular Transformer models, and benchmarked against $\mathrm{L}_{0}$ regularization and Variational Dropout on convolutional neural networks. Experimental results demonstrate substantial model compression without degrading predictive performance, and in some cases improving it. On MNIST, the gated MLP retains only 14% of weights (86% pruning) while maintaining 98% accuracy. The gated DNN and Tab Transformer similarly outperform their dense counterparts while retaining only 46% and 46.22% of parameters, respectively. Benchmarking further shows that the proposed adaptive gating achieves competitive or superior accuracy-sparsity trade-offs compared with $\mathbf{L}_{0}$ regularization and Variational Dropout. These results demonstrate that the proposed dynamically adaptive gating framework provides an efficient and interpretable pathway for sparsity learning, enabling high-performing lightweight neural network deployment.
Raunak Dev, Mydhily Sankar, Devaprabha Biju S et al.· 2026 International Conferenc...· 0 citations
It is demonstrated that network architecture and its coeficients can be learned together by unifying concepts of evolutionary search within a population based traditional training process.
Indicative in these results is that intelligent batch ordering can partially compensate for reduced architectural complexity, thus offering a computationally efficient alternative to deeper models.
Neural Architecture Search (NAS) aims to identify high-performance networks within a defined search space. Training-free metrics have been proposed to estimate network performance without actual training, reducing NAS deployment costs. However, individual training-free metrics often capture only partial architectural features, and their estimation capabilities are different in various tasks. Combining multiple training-free metrics has been explored to enhance scalability across tasks. Yet, these methods typically optimize global metric combinations over the entire search space, overlooking the varying sensitivities of different architectures to specific metrics, which may limit the final architectures’ performance. To address these challenges, we propose the Per-Architecture Training-Free Metric Optimization NAS (PO-NAS) algorithm. This algorithm: (a) Integrates multiple training-free metrics as auxiliary scores, dynamically optimizing their combinations using limited real-time training data, without relying on benchmarks; (b) Individually optimizes metric combinations for each architecture; (c) Integrates an evolutionary algorithm that leverages efficient predictions from the surrogate model, enhancing search efficiency in large search spaces. Notably, PO-NAS combines the efficiency of training-free search with the robust performance of training-based evaluations. Extensive experiments demonstrate the effectiveness of our approach. Our code has been made publicly available at https://github.com/LMZ-Zhuo/PO-NAS .
Mingzhuo Lin, Jianpin Luo· Neural Information Processin...· 2 citations
It is demonstrated that hyperparameter optimization dynamics depend heavily on dataset complexity, where computational efficiency is the primary differentiator for simpler classification tasks, but optimization architecture selection becomes critical for navigating challenging medical imaging applications.
Sarab Almuhaideb, Ahmad Raza Khan· Applied Sciences· 0 citations
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