ASNN: Inverse Design for Neural Architectures

• The paper presents an inverse-design method that predicts neural network layer widths from performance distributions, outperforming original grid configurations.
• ASNN leverages iterative prediction and retraining using repeated evaluations to progressively refine architecture suggestions with empirical improvements.
• The approach circumvents exhaustive search by training on repeated accuracy trials, using performance variance as a signal for architecture optimization.

Architecture Suggesting Neural Network (ASNN) denotes a model that learns an inverse relationship between neural-network performance and neural-network structure: instead of predicting accuracy from an architecture, it uses observed test-accuracy distributions as input and outputs architectural parameters that are expected to yield improved performance. In the formulation reported in "ASNN: Learning to Suggest Neural Architectures from Performance Distributions" (Hong, 27 Jul 2025), ASNN is trained on repeated empirical evaluations of small fully connected networks and then used in an iterative prediction-and-retraining loop to suggest new architectures. The method is positioned as an alternative to heuristic, manual, random-search-based, or heavier neural architecture search procedures, with the specific claim that it can learn a performance-structure relationship from repeated trials and use that relationship to propose architectures that outperform the best configurations present in its original training data (Hong, 27 Jul 2025).

1. Conceptual definition and formalization

ASNN is defined as a learned inverse model for architecture design. Its central premise is that there is no simple closed-form relationship between neural-network structure and performance, so architecture design is usually handled by heuristics or search. ASNN addresses this by learning from empirical architecture-performance pairs, but in a reversed direction: accuracy statistics are the input, and architectural parameters are the output (Hong, 27 Jul 2025).

The paper formalizes the model as

$$\mathrm{ASNN}: \mathbb{R}^{10} \to \mathbb{R}^{2} \quad \text{or} \quad \mathrm{ASNN}: \mathbb{R}^{10} \to \mathbb{R}^{3},$$

depending on whether the target architecture has two or three hidden layers. Conceptually, the input is a 10-dimensional vector of test accuracies produced by repeated stochastic training runs, and the output is a tuple of layer widths. This is explicitly distinguished from standard performance-prediction models: ASNN is not primarily estimating accuracy from structure, but learning an inverse mapping from performance distributions back to architecture (Hong, 27 Jul 2025).

The paper also draws an analogy to reinforcement learning. In that framing, the network configuration functions as a “state,” the structural choice functions as an “action,” and the role of the “Q-value” is played by a performance metric such as test accuracy. This analogy is descriptive rather than formal; the paper’s main mathematical content remains the inverse mapping from an accuracy vector to a low-dimensional architecture parameterization (Hong, 27 Jul 2025).

A plausible implication is that ASNN should be understood less as a conventional NAS controller and more as an inverse-design model or suggestion engine. Its operational target is not exhaustive search over a predefined architecture space, but direct proposal of a candidate structure from empirical performance behavior.

2. Data construction and representation of performance distributions

The training data for ASNN are generated from TensorFlow-based models derived from the official TensorFlow introduction example code. In the reported setup, the number of training epochs was increased from 10 to 50, the optimizer was Adam, and dropout was included. Because the training process is stochastic, each architecture was trained 10 times, and the 10 resulting test accuracies were recorded as the ASNN input vector (Hong, 27 Jul 2025).

Two architecture families were used. In the 2-layer case, each hidden layer size was chosen from

$\{16, 32, 64, 128, 256\},$

yielding $5 \times 5 = 25$ configurations. With 10 trials per configuration, this produced 250 accuracy measurements. In the 3-layer case, each layer size was chosen from

$\{16, 32, 64, 128\},$

yielding $4 \times 4 \times 4 = 64$ configurations and 640 total accuracy measurements from 10 trials per configuration (Hong, 27 Jul 2025).

The dataset format is unusual by design. Each sample contains architectural parameters together with 10 observed accuracies, but ASNN training uses the 10 accuracies as inputs and the architectural parameters as outputs. The paper states that the data were randomly shuffled, motivated by the assumption that the accuracy samples are i.i.d. and that ordering should not matter. The original datasets of 250 and 640 samples were then expanded by randomly shuffling the accuracy values, producing approximately 10,000 samples. In addition, the input accuracies were multiplied by 100: $$\text{scaled accuracy} = 100 \times \text{accuracy},$$ with the stated purpose of stabilizing and speeding up training (Hong, 27 Jul 2025).

This representation makes repeated-trial variability a first-class object. Rather than collapsing stochastic training outcomes to a single mean, ASNN treats the 10-sample accuracy distribution itself as the signal from which structural information is inferred.

3. Training procedure and iterative architecture suggestion

The reported ASNN workflow is iterative. First, ASNN is trained on an initial dataset $D$ of architecture-performance pairs. Then a high target-accuracy vector,

$\mathbf{x} = (100,100,\dots,100),$

is fed to the trained ASNN. The network predicts an architecture $A$, after which the original task network is instantiated with architecture $A$, trained, and evaluated over 10 trials to obtain

$\{16, 32, 64, 128, 256\},$0

These new observations are appended to the dataset as

$\{16, 32, 64, 128, 256\},$1

and ASNN is retrained before the next round (Hong, 27 Jul 2025).

The paper describes this as an iterative prediction-retraining loop and reports five iterative rounds. Its rationale is that the learned performance-structure relationship can be progressively refined as new data are gathered around promising regions of architecture space. The procedure is empirical rather than theoretically formalized: the paper does not provide an explicit loss such as a written MSE or cross-entropy objective for ASNN itself, and it does not derive a detailed probabilistic model beyond the assumptions that the 10 accuracies per architecture are i.i.d. samples from a stochastic performance distribution and that random shuffling approximates exchangeability (Hong, 27 Jul 2025).

This suggests that ASNN relies heavily on the informativeness of the sampled performance distribution. Its architecture proposals are conditioned not on a symbolic description of the task or a hand-designed search policy, but on how prior architectures behave under repeated stochastic training.

4. Reported empirical results

The paper reports that ASNN successfully suggested architectures that outperformed the best results found in the original training data in both the 2-layer and 3-layer settings (Hong, 27 Jul 2025). The following summary condenses the reported search spaces and best outcomes.

Setting	Original architecture space	Best reported ASNN suggestion
2-layer	$\{16, 32, 64, 128, 256\},$2; best original accuracy around 0.9831	$\{16, 32, 64, 128, 256\},$3, mean accuracy 0.9838
3-layer	$\{16, 32, 64, 128, 256\},$4; best original accuracy 0.9817	$\{16, 32, 64, 128, 256\},$5, mean accuracy 0.98313

In the 2-layer case, the reported ASNN predictions were $\{16, 32, 64, 128, 256\},$6, $\{16, 32, 64, 128, 256\},$7, $\{16, 32, 64, 128, 256\},$8, $\{16, 32, 64, 128, 256\},$9, and $5 \times 5 = 25$0, with mean accuracies $5 \times 5 = 25$1, $5 \times 5 = 25$2, $5 \times 5 = 25$3, $5 \times 5 = 25$4, and $5 \times 5 = 25$5, respectively. The best result in the original 2-layer training data was around $5 \times 5 = 25$6, so several of the suggested architectures exceeded the original grid maximum. In the 3-layer case, the reported predictions were $5 \times 5 = 25$7, $5 \times 5 = 25$8, $5 \times 5 = 25$9, $\{16, 32, 64, 128\},$0, and $\{16, 32, 64, 128\},$1, with mean accuracies $\{16, 32, 64, 128\},$2, $\{16, 32, 64, 128\},$3, $\{16, 32, 64, 128\},$4, $\{16, 32, 64, 128\},$5, and $\{16, 32, 64, 128\},$6, respectively. The original best 3-layer training accuracy was $\{16, 32, 64, 128\},$7, and the best predicted architecture improved on it by approximately $\{16, 32, 64, 128\},$8 (Hong, 27 Jul 2025).

Two points are especially significant. First, the predicted architectures are not confined to the small discrete grids used to generate the original data. This suggests that the learned inverse map was used extrapolatively rather than merely memorizing the enumerated grid points. Second, the paper reports that repeated prediction and retraining cycles generally improved architecture quality over five rounds, which is presented as evidence that ASNN can progressively refine its approximation of the performance landscape (Hong, 27 Jul 2025).

The empirical scope, however, remains narrow. The reported gains are specific to small 2-layer and 3-layer fully connected settings, and the evidence is based on mean test accuracies under repeated trials rather than on broader benchmark suites.

5. Relation to neural architecture search and other architecture-suggesting methods

ASNN belongs to a broader class of methods that make architecture design itself learnable, but it differs materially from mainstream NAS formulations. In differentiable NAS for MRI reconstruction, for example, EMR-NAS searches over a block-level operation space within a residual-in-residual cascade and uses bilevel optimization to choose among eight candidate operations, producing a dataset-specific heterogeneous network (Huang et al., 2020). That approach still searches architectures directly and trains architecture parameters jointly with network weights. By contrast, ASNN learns from previously observed architecture-performance pairs and predicts an architecture from an accuracy vector.

A second point of comparison is graph-based NAS. "Network Graph Based Neural Architecture Search" predicts architecture quality from graph properties and uses greedy rewiring over relational graphs to improve predicted performance, with a linear-regression surrogate over graph-theoretic descriptors such as path length, clustering, and centrality (Huang et al., 2021). ASNN does not reason over graph topology in that way. Its representation of architectural information is much simpler—layer widths in 2-layer or 3-layer fully connected networks—and its conditioning signal is a performance distribution rather than graph features.

These comparisons place ASNN in a distinct methodological niche. It is lighter than methods that repeatedly search and retrain large operator-level or graph-level spaces, but it is also much narrower in representational scope. The paper itself suggests potential integration with NAS and AutoML, which indicates that ASNN is best interpreted as a complement or lightweight alternative to search-heavy architecture optimization rather than a replacement for full-scale NAS (Hong, 27 Jul 2025).

6. Terminology, scope, and acronym ambiguity

The acronym “ASNN” is not unique in the literature, and disambiguation is necessary. In "Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks," ASNN stands for Adapting Spiking Neural Network, a framework in which adaptive spiking neurons replace ReLU units in feedforward and convolutional ANNs (Zambrano et al., 2016). In "Generalized adaptive smoothing based neural network architecture for traffic state estimation," ASNN stands for Adaptive Smoothing Neural Network, a neural-network reinterpretation of the Adaptive Smoothing Method for traffic-state reconstruction (Yang et al., 2023). In "Ensemble plasticity and network adaptability in SNNs," the acronym ASNN is used for Artificial Spiking Neural Network, and the paper describes an architecture-suggesting mechanism based on entropy-regulated intrinsic plasticity and spike-rate pruning rather than the inverse performance-to-architecture mapping used in Architecture Suggesting Neural Network (Weerasinghe et al., 2022).

Within the specific meaning of Architecture Suggesting Neural Network, the reported limitations are explicit. The experiments are restricted to very small, discrete architecture grids; only 2-layer and 3-layer fully connected architectures are studied; the method depends on repeated evaluation and stochastic averaging; no detailed theoretical loss function or rigorous probabilistic derivation is provided; the architecture space is manually defined and finite; and the reported improvement is empirical rather than supported by broad benchmarking (Hong, 27 Jul 2025).

The stated future direction is integration with NAS, AutoML, and automated iterative architecture optimization. A plausible implication is that any broader applicability of ASNN will depend on whether the inverse mapping from performance distributions to structure can be extended beyond small width tuples to richer architectural objects such as block compositions, graphs, or operator choices. In its present form, ASNN is best characterized as a narrowly scoped but conceptually distinctive inverse-design model for architecture suggestion, grounded in empirical performance distributions rather than direct architecture search (Hong, 27 Jul 2025).