H-Model: Dynamic Neural Architectures for Adaptive Processing

• URL: http://arxiv.org/abs/2511.11669v1
• Date: Tue, 11 Nov 2025 14:39:42 GMT
• Title: H-Model: Dynamic Neural Architectures for Adaptive Processing
• Authors: Dmytro Hospodarchuk,
• Abstract summary: This article explores the design and experimentation of a neural network architecture capable of dynamically adjusting its internal structure based on the input data.<n>The proposed model introduces a routing mechanism that allows each layer to influence how its outputs are propagated through the network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article explores the design and experimentation of a neural network architecture capable of dynamically adjusting its internal structure based on the input data. The proposed model introduces a routing mechanism that allows each layer to influence how its outputs are propagated through the network, enabling iterative and adaptive computation. This concept is loosely inspired by the idea of thought processes and dynamic reasoning, where information flow is conditioned not only on the data itself, but also on the internal state of the system. It is important to note that this work does not aim to compete with state-of-the-art language models in terms of performance. Instead, it presents a conceptual prototype-an architectural framework that opens up a new direction for exploring adaptable and potentially more interpretable networks. The goal is not optimization of existing benchmarks but rather the proposal of a system that can learn not only representations, but also the structure of computation itself. Due to practical constraints in computing resources and data, this study remains a preliminary investigation. Nevertheless, initial observations show promise, and the architecture's full potential can only be evaluated in future experiments under more favorable computational conditions.

Related papers

• Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction [57.19302613163439]
  We introduce neural network reprogrammability as a unifying framework for model adaptation.<n>We present a taxonomy that categorizes such information manipulation approaches across four key dimensions.<n>We also analyze remaining technical challenges and ethical considerations.
  arXiv  Detail & Related papers  (2025-06-05T05:42:27Z)
• Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures [49.19753720526998]
  We derive theoretical scaling laws for neural network performance on synthetic datasets.<n>We validate that convolutional networks, whose structure aligns with that of the generative process through locality and weight sharing, enjoy a faster scaling of performance.<n>This finding clarifies the architectural biases underlying neural scaling laws and highlights how representation learning is shaped by the interaction between model architecture and the statistical properties of data.
  arXiv  Detail & Related papers  (2025-05-11T17:44:14Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Advancing Neural Network Performance through Emergence-Promoting Initialization Scheme [0.0]
  Emergence in machine learning refers to the spontaneous appearance of capabilities that arise from the scale and structure of training data.<n>We introduce a novel yet straightforward neural network initialization scheme that aims at achieving greater potential for emergence.<n>We demonstrate substantial improvements in both model accuracy and training speed, with and without batch normalization.
  arXiv  Detail & Related papers  (2024-07-26T18:56:47Z)
• Training Neural Networks with Internal State, Unconstrained Connectivity, and Discrete Activations [66.53734987585244]
  True intelligence may require the ability of a machine learning model to manage internal state. We show that we have not yet discovered the most effective algorithms for training such models. We present one attempt to design such a training algorithm, applied to an architecture with binary activations and only a single matrix of weights.
  arXiv  Detail & Related papers  (2023-12-22T01:19:08Z)
• NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction [37.357949900603295]
  We propose a neural architecture representation model that can be used to estimate attributes holistically. Experiment results show that our proposed framework can be used to predict the latency and accuracy attributes of both cell architectures and whole deep neural networks.
  arXiv  Detail & Related papers  (2022-11-15T10:15:21Z)
• The Neural Race Reduction: Dynamics of Abstraction in Gated Networks [12.130628846129973]
  We introduce the Gated Deep Linear Network framework that schematizes how pathways of information flow impact learning dynamics. We derive an exact reduction and, for certain cases, exact solutions to the dynamics of learning. Our work gives rise to general hypotheses relating neural architecture to learning and provides a mathematical approach towards understanding the design of more complex architectures.
  arXiv  Detail & Related papers  (2022-07-21T12:01:03Z)
• Dynamic Inference with Neural Interpreters [72.90231306252007]
  We present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules. inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. We show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner.
  arXiv  Detail & Related papers  (2021-10-12T23:22:45Z)
• A Semi-Supervised Assessor of Neural Architectures [157.76189339451565]
  We employ an auto-encoder to discover meaningful representations of neural architectures. A graph convolutional neural network is introduced to predict the performance of architectures.
  arXiv  Detail & Related papers  (2020-05-14T09:02:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.