Modular Neural Networks

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Modular neural networks study how to divide computation among specialized components and coordinate them to solve a task. Common approaches include gated mixtures of experts, branch-based networks, task-specific parameter masks, and compositional neural operators for physical systems. These designs aim to improve reuse, interpretability, efficiency, and continual learning by limiting interference between tasks or domains. A central tension is that specialization can help when module boundaries match the task, but effective routing and communication are difficult, and too much separation can limit useful sharing.

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74 papers9 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

RF-LEGO: Modularized Signal Processing-Deep Learning Co-Design for RF Sensing via Deep Unrolling
Apr 11, 2026Luca Jiang-Tao Yu, Chenshu WuDeep Unfolding Modular Neural Networks
The University of Hong Kong
Associative Memory using Attribute-Specific Neuron Groups-2: Learning and Sequential Associative Recall between Cue Neurons for different Cue Balls
Mar 26, 2026Hiroshi InazawaAssociative Memory Modular Neural Networks
Center for Education in Information Systems, Kobe Shoin Women’s University１, 1-2-1 Shinohara-Obanoyama, Nada Kobe 657-0015, Japan.
Neural Network Models for Contextual Regression
Mar 25, 2026Seksan Kiatsupaibul, Pakawan ChansiripasModular Neural Networks Neural Network Interpretability
Department of Statistics, Chulalongkorn University, Bangkok 10330, Thailand
A Cortically Inspired Architecture for Modular Perceptual AI
Mar 7, 2026Prerna LuthraModular Neural Networks Neural Network Interpretability
Independent Researcher
Modular Neural Computer
Mar 4, 2026Florin LeonNeural Algorithmic Reasoning Memory-Augmented Neural Networks
Department of Computers Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iași, Romania
Riemannian Optimization in Modular Systems
Mar 4, 2026Christian Pehle, Jean-Jacques SlotineContraction Theory Riemannian Optimization
Cold Spring Harbor Laboratory Cold Spring Harbor, NY 11724 · Massachusetts Institute of Technology Cambridge, MA 02139
Learning Physical Operators using Neural Operators
Feb 26, 2026Vignesh Gopakumar, Ander Gray, Dan Giles +5PDE Surrogate Modeling Physics-Informed Neural Operators
UCL Centre for Artificial Intelligence · UK Atomic Energy Authority · LIX, CNRS, ´Ecole Polytechnique +2
Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns
Feb 25, 2026Afshin KhadangiContinual Learning for LLMs Decoder-Only Language Models
SnT, University of Luxembourg
Modularity is the Bedrock of Natural and Artificial Intelligence
Feb 21, 2026Alessandro SalatielloModular Neural Networks
Department of Computer Science, University of Tübingen
A Theoretical Framework for Modular Learning of Robust Generative Models
Feb 19, 2026Corinna Cortes, Mehryar Mohri, Yutao ZhongGenerative Modeling Modular Neural Networks
Google Research, New York, NY · Courant Institute of Mathematical Sciences, New York, NY
Unraveling the cognitive patterns of Large Language Models through module communities
Aug 25, 2025Kushal Raj Bhandari, Pin-Yu Chen, Jianxi GaoComputational Cognitive Modeling LLM Interpretability
Department of Computer Science Rensselaer Polytechnic Institute Troy, NY, USA · IBM Research Yorktown Heights, NY, USA
Effects of relational graph modularity and depth on the learning performance of neural networks
Jul 14, 2025Yash Arya, Sang Hoon LeeModular Neural Networks
Department of Physics, Gyeongsang National University, Jinju, 52828, Korea
Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
Jun 10, 2025Laurens Engwegen, Max Weltevrede, Caroline Horsch +2Robotic Control Reinforcement Learning
Delft University of Technology Delft, The Netherlands
Fourier Multi-Component and Multi-Layer Neural Networks: Unlocking High-Frequency Potential
Feb 26, 2025Shijun Zhang, Hongkai Zhao, Yimin Zhong +1Fourier Feature Embeddings Neural Network Optimization
Department of Applied Mathematics Hong Kong Polytechnic University · Department of Mathematics Duke University · Department of Mathematics and Statistics Auburn University +1

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RF-LEGO: Modularized Signal Processing-Deep Learning Co-Design for RF Sensing via Deep Unrolling

Luca Jiang-Tao Yu, Chenshu Wu

The University of Hong Kong

Deep Unfolding Modular Neural Networks Wi-Fi Sensing

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Wireless sensing, traditionally relying on signal processing (SP) techniques, has recently shifted toward data-driven deep learning (DL) to achieve performance breakthroughs. However, existing deep wireless sensing models are typically end-to-end and task-specific, lacking reusability and interpretability. We propose RF-LEGO, a modular co-design framework that transforms interpretable SP algorithms into trainable, physics-grounded DL modules through deep unrolling. By replacing hand-tuned parameters with learnable ones while preserving core processing structures and mathematical operators, RF-LEGO ensures modularity, cascadability, and structure-aligned interpretability. Specifically, we introduce three deep-unrolled modules for critical RF sensing tasks: frequency transform, spatial angle estimation, and signal detection. Extensive experiments using real-world data for Wi-Fi, millimeter-wave, UWB, and 6G sensing demonstrate that RF-LEGO significantly outperforms existing SP and DL baselines, both standalone and when integrated into multiple downstream tasks. RF-LEGO pioneers a novel SP-DL co-design paradigm for wireless sensing via deep unrolling, shedding light on efficient and interpretable deep wireless sensing solutions. Our code is available at https://github.com/aiot-lab/RF-LEGO.