Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition

• URL: http://arxiv.org/abs/2506.22443v1
• Date: Wed, 11 Jun 2025 11:30:48 GMT
• Title: Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
• Authors: Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille,
• Abstract summary: Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency.<n>This work investigates a neuro-symbolic rule learning neural network named RL-Net that learns interpretable rule lists.<n>We benchmark RL-Net against a fully transparent rule-based system (MIRA) and an explainable black-box model (XentricAI)<n>Our results show that RL-Net achieves a favorable trade-off, maintaining strong performance (93.03% F1) while significantly reducing rule complexity.
• Score: 2.99664686845172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbolic rule learning neural network named RL-Net that learns interpretable rule lists through neural optimization, applied for the first time to radar-based hand gesture recognition (HGR). We benchmark RL-Net against a fully transparent rule-based system (MIRA) and an explainable black-box model (XentricAI), evaluating accuracy, interpretability, and user adaptability via transfer learning. Our results show that RL-Net achieves a favorable trade-off, maintaining strong performance (93.03% F1) while significantly reducing rule complexity. We identify optimization challenges specific to rule pruning and hierarchy bias and propose stability-enhancing modifications. Compared to MIRA and XentricAI, RL-Net emerges as a practical middle ground between transparency and performance. This study highlights the real-world feasibility of neuro-symbolic models for interpretable HGR and offers insights for extending explainable AI to edge-deployable sensing systems.

Related papers

• Leveraging chaos in the training of artificial neural networks [3.379574469735166]
  We explore the dynamics of the neural network trajectory along training for unconventionally large learning rates.<n>We show that for a region of values of the learning rate, the GD optimization shifts away from purely exploitation-like algorithm into a regime of exploration-exploitation balance.
  arXiv  Detail & Related papers  (2025-06-10T07:41:58Z)
• High-order Regularization for Machine Learning and Learning-based Control [4.5375744653674275]
  The paper proposes a novel regularization procedure for machine learning.<n>The proposed HR method ensures the provable convergence of the approximation algorithm.<n>We prove that the generalizability of neural networks can be maximized with a proper regularization matrix.
  arXiv  Detail & Related papers  (2025-05-13T00:00:23Z)
• Global Convergence and Rich Feature Learning in $L$-Layer Infinite-Width Neural Networks under $μ$P Parametrization [66.03821840425539]
  In this paper, we investigate the training dynamics of $L$-layer neural networks using the tensor gradient program (SGD) framework.<n>We show that SGD enables these networks to learn linearly independent features that substantially deviate from their initial values.<n>This rich feature space captures relevant data information and ensures that any convergent point of the training process is a global minimum.
  arXiv  Detail & Related papers  (2025-03-12T17:33:13Z)
• Neural network interpretability with layer-wise relevance propagation: novel techniques for neuron selection and visualization [0.49478969093606673]
  We present a novel approach that improves the parsing of selected neurons during.<n>LRP backward propagation, using the Visual Geometry Group 16 (VGG16) architecture as a case study.<n>Our approach enhances interpretability and supports the development of more transparent artificial intelligence (AI) systems for computer vision applications.
  arXiv  Detail & Related papers  (2024-12-07T15:49:14Z)
• DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models [1.747623282473278]
  We introduce DLBacktrace, a model-agnostic technique designed to provide clear insights into deep learning model decisions.<n>We present a comprehensive overview of DLBacktrace and benchmark its performance against established interpretability methods.<n>Our results demonstrate that DLBacktrace effectively enhances understanding of model behavior across diverse tasks.
  arXiv  Detail & Related papers  (2024-11-19T16:54:30Z)
• ReLUs Are Sufficient for Learning Implicit Neural Representations [17.786058035763254]
  We revisit the use of ReLU activation functions for learning implicit neural representations. Inspired by second order B-spline wavelets, we incorporate a set of simple constraints to the ReLU neurons in each layer of a deep neural network (DNN) We demonstrate that, contrary to popular belief, one can learn state-of-the-art INRs based on a DNN composed of only ReLU neurons.
  arXiv  Detail & Related papers  (2024-06-04T17:51:08Z)
• An Interpretable Power System Transient Stability Assessment Method with Expert Guiding Neural-Regression-Tree [12.964139269555277]
  An interpretable power system Transient Stability Assessment method with Expert guiding Neural-Regression-Tree (TSA-ENRT) is proposed. TSA-ENRT utilizes an expert guiding nonlinear regression tree to approximate the neural network prediction and the neural network can be explained by the interpretive rules generated by the tree model. Extensive experiments indicate the interpretive rules generated by the proposed TSA-ENRT are highly consistent with the neural network prediction and more agreed with human expert cognition.
  arXiv  Detail & Related papers  (2024-04-03T08:22:41Z)
• NeuralFastLAS: Fast Logic-Based Learning from Raw Data [54.938128496934695]
  Symbolic rule learners generate interpretable solutions, however they require the input to be encoded symbolically. Neuro-symbolic approaches overcome this issue by mapping raw data to latent symbolic concepts using a neural network. We introduce NeuralFastLAS, a scalable and fast end-to-end approach that trains a neural network jointly with a symbolic learner.
  arXiv  Detail & Related papers  (2023-10-08T12:33:42Z)
• Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation [49.44309457870649]
  Layer-wise Feedback feedback (LFP) is a novel training principle for neural network-like predictors.<n>LFP decomposes a reward to individual neurons based on their respective contributions.<n>Our method then implements a greedy reinforcing approach helpful parts of the network and weakening harmful ones.
  arXiv  Detail & Related papers  (2023-08-23T10:48:28Z)
• ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction [82.81767856234956]
  This paper proposes a new learning framework named ConCerNet to improve the trustworthiness of the DNN based dynamics modeling. We show that our method consistently outperforms the baseline neural networks in both coordinate error and conservation metrics.
  arXiv  Detail & Related papers  (2023-02-11T21:07:30Z)
• Neural networks trained with SGD learn distributions of increasing complexity [78.30235086565388]
  We show that neural networks trained using gradient descent initially classify their inputs using lower-order input statistics. We then exploit higher-order statistics only later during training. We discuss the relation of DSB to other simplicity biases and consider its implications for the principle of universality in learning.
  arXiv  Detail & Related papers  (2022-11-21T15:27:22Z)
• A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification [9.365794791156972]
  This paper introduces a new Lightweight, Efficient and eXplainable-by-design convolutional neural network (LEXNet) for Internet traffic classification. LEXNet relies on a new residual block (for lightweight and efficiency purposes) and prototype layer (for explainability) Based on a commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain the same accuracy as the best performing state-of-the-art neural network.
  arXiv  Detail & Related papers  (2022-02-11T10:21:34Z)
• Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks [79.74580058178594]
  We analyze the performance of training a pruned neural network by analyzing the geometric structure of the objective function. We show that the convex region near a desirable model with guaranteed generalization enlarges as the neural network model is pruned.
  arXiv  Detail & Related papers  (2021-10-12T01:11:07Z)

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.