Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing
- URL: http://arxiv.org/abs/2602.22115v1
- Date: Wed, 25 Feb 2026 17:01:52 GMT
- Title: Slice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing
- Abstract summary: This paper proposes an approach leveraging domain slicing to facilitate explanation generation for NNs.<n>By reducing the complexity of logical constraints through slicing, we decrease explanation time by up to 40% less time.
- Score: 3.847524185858632
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Neural networks (NNs) are pervasive across various domains but often lack interpretability. To address the growing need for explanations, logic-based approaches have been proposed to explain predictions made by NNs, offering correctness guarantees. However, scalability remains a concern in these methods. This paper proposes an approach leveraging domain slicing to facilitate explanation generation for NNs. By reducing the complexity of logical constraints through slicing, we decrease explanation time by up to 40\% less time, as indicated through comparative experiments. Our findings highlight the efficacy of domain slicing in enhancing explanation efficiency for NNs.
Related papers
- Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks [2.753173444623078]
This paper introduces a flexible symbolic framework for an efficient, guided computation of explanations of the NN behavior.<n>We show that our method yields explanations that are not restricted in shape.<n> Notably, our approach enables logical explanation of deep networks not amenable to prior logic-based methods.
arXiv Detail & Related papers (2026-09-12T18:50:37Z) - Bound Propagation meets Constraint Simplification: Improving Logic-based XAI for Neural Networks [1.0499611180329804]
Logic-based methods for explaining neural network decisions often suffer from high computational costs.<n>In this work, we improve the efficiency of such methods by combining bound propagation with constraint simplification.<n>Our experiments suggest that combining these techniques reduces explanation time by up to 89.26%, particularly for larger neural networks.
arXiv Detail & Related papers (2026-03-02T14:36:31Z) - Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations [6.902279764206365]
We propose a novel abstraction-refinement technique for efficiently computing provably sufficient explanations of neural network predictions.<n>Our approach enhances the efficiency of obtaining provably sufficient explanations for neural network predictions while additionally providing a fine-grained interpretation of the network's predictions across different abstraction levels.
arXiv Detail & Related papers (2025-06-10T07:04:13Z) - ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph
Neural Networks [7.077341403454516]
Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery.
To address this challenge and enable reliable decision-making, many GNN explainers have been proposed in recent years.
We introduce Auxiliary Generative Adrative Network (ACGAN) into the field of GNN explanation and propose a new GNN explainer dubbedemphACGANGNNExplainer.
arXiv Detail & Related papers (2023-09-29T01:20:28Z) - Learning Low Dimensional State Spaces with Overparameterized Recurrent
Neural Nets [57.06026574261203]
We provide theoretical evidence for learning low-dimensional state spaces, which can also model long-term memory.
Experiments corroborate our theory, demonstrating extrapolation via learning low-dimensional state spaces with both linear and non-linear RNNs.
arXiv Detail & Related papers (2022-10-25T14:45:15Z) - Towards Formal Approximated Minimal Explanations of Neural Networks [0.0]
Deep neural networks (DNNs) are now being used in numerous domains.
DNNs are "black-boxes", and cannot be interpreted by humans.
We propose an efficient, verification-based method for finding minimal explanations.
arXiv Detail & Related papers (2022-10-25T11:06:37Z) - Zonotope Domains for Lagrangian Neural Network Verification [102.13346781220383]
We decompose the problem of verifying a deep neural network into the verification of many 2-layer neural networks.
Our technique yields bounds that improve upon both linear programming and Lagrangian-based verification techniques.
arXiv Detail & Related papers (2022-10-14T19:31:39Z) - On the Intrinsic Structures of Spiking Neural Networks [66.57589494713515]
Recent years have emerged a surge of interest in SNNs owing to their remarkable potential to handle time-dependent and event-driven data.
There has been a dearth of comprehensive studies examining the impact of intrinsic structures within spiking computations.
This work delves deep into the intrinsic structures of SNNs, by elucidating their influence on the expressivity of SNNs.
arXiv Detail & Related papers (2022-06-21T09:42:30Z) - On Consistency in Graph Neural Network Interpretation [34.25952902469481]
Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions.
Various algorithms are proposed, but most of them formalize this task by searching the minimal subgraph.
We propose a simple yet effective countermeasure by aligning embeddings.
arXiv Detail & Related papers (2022-05-27T02:58:07Z) - Comparative Analysis of Interval Reachability for Robust Implicit and
Feedforward Neural Networks [64.23331120621118]
We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs)
INNs are a class of implicit learning models that use implicit equations as layers.
We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
arXiv Detail & Related papers (2022-04-01T03:31:27Z) - Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks [65.23508422635862]
We propose learning rules with the recently proposed logical neural networks (LNN)
Compared to others, LNNs offer strong connection to classical Boolean logic.
Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable.
arXiv Detail & Related papers (2021-12-06T19:38:30Z) - Reinforcement Learning with External Knowledge by using Logical Neural
Networks [67.46162586940905]
A recent neuro-symbolic framework called the Logical Neural Networks (LNNs) can simultaneously provide key-properties of both neural networks and symbolic logic.
We propose an integrated method that enables model-free reinforcement learning from external knowledge sources.
arXiv Detail & Related papers (2021-03-03T12:34:59Z) - A Chain Graph Interpretation of Real-World Neural Networks [58.78692706974121]
We propose an alternative interpretation that identifies NNs as chain graphs (CGs) and feed-forward as an approximate inference procedure.
The CG interpretation specifies the nature of each NN component within the rich theoretical framework of probabilistic graphical models.
We demonstrate with concrete examples that the CG interpretation can provide novel theoretical support and insights for various NN techniques.
arXiv Detail & Related papers (2020-06-30T14:46:08Z)
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.