Triadic Concept Analysis for Logic Interpretation of Simple Artificial Networks

• URL: http://arxiv.org/abs/2601.06229v1
• Date: Fri, 09 Jan 2026 11:36:22 GMT
• Title: Triadic Concept Analysis for Logic Interpretation of Simple Artificial Networks
• Abstract summary: An artificial neural network (ANN) is a numerical method used to solve complex classification problems.<n>Our idea is to derive a symbolic representation from a simple ANN model trained on minterm values of input objects.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: An artificial neural network (ANN) is a numerical method used to solve complex classification problems. Due to its high classification power, the ANN method often outperforms other classification methods in terms of accuracy. However, an ANN model lacks interpretability compared to methods that use the symbolic paradigm. Our idea is to derive a symbolic representation from a simple ANN model trained on minterm values of input objects. Based on ReLU nodes, the ANN model is partitioned into cells. We convert the ANN model into a cell-based, three-dimensional bit tensor. The theory of Formal Concept Analysis applied to the tensor yields concepts that are represented as logic trees, expressing interpretable attribute interactions. Their evaluations preserve the classification power of the initial ANN model.

Related papers

• Unveiling Ontological Commitment in Multi-Modal Foundation Models [7.485653059927206]
  Deep neural networks (DNNs) automatically learn rich representations of concepts and respective reasoning. We propose a method that extracts the learned superclass hierarchy from a multimodal DNN for a given set of leaf concepts. An initial evaluation study shows that meaningful ontological class hierarchies can be extracted from state-of-the-art foundation models.
  arXiv  Detail & Related papers  (2024-09-25T17:24:27Z)
• Logic interpretations of ANN partition cells [0.0]
  Consider a binary classification problem solved using a feed-forward artificial neural network (ANN) Let the ANN be composed of a ReLU layer and several linear layers (convolution, sum-pooling, or fully connected) We construct a bridge between a simple ANN and logic. As a result, we can analyze and manipulate the semantics of an ANN using the powerful tool set of logic.
  arXiv  Detail & Related papers  (2024-08-26T14:43:43Z)
• Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks [79.16635054977068]
  We present a new class of equivariant neural networks, dubbed Lattice-Equivariant Neural Networks (LENNs) Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators. Our work opens towards practical utilization of machine learning-augmented Lattice Boltzmann CFD in real-world simulations.
  arXiv  Detail & Related papers  (2024-05-22T17:23:15Z)
• A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data [77.34726150561087]
  This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex sensory data. The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept. The model is able to produce fairly rich yet human-readable conceptual representations without training.
  arXiv  Detail & Related papers  (2023-07-16T15:59:13Z)
• Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean [1.0923877073891446]
  Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks. Despite this success, the inner workings of DNNs are often not transparent. This lack of interpretability has led to increased research on inherently interpretable neural networks.
  arXiv  Detail & Related papers  (2023-01-27T17:06:13Z)
• Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance [10.57079240576682]
  This study proposes a novel approach that combines theory and data-driven choice models using Artificial Neural Networks (ANNs) In particular, we use continuous vector representations, called embeddings, for encoding categorical or discrete explanatory variables. Our models deliver state-of-the-art predictive performance, outperforming existing ANN-based models while drastically reducing the number of required network parameters.
  arXiv  Detail & Related papers  (2021-09-24T15:55:31Z)
• Neural Unsupervised Semantic Role Labeling [48.69930912510414]
  We present the first neural unsupervised model for semantic role labeling. We decompose the task as two argument related subtasks, identification and clustering. Experiments on CoNLL-2009 English dataset demonstrate that our model outperforms previous state-of-the-art baseline.
  arXiv  Detail & Related papers  (2021-04-19T04:50:16Z)
• Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking [63.49779304362376]
  Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. We introduce a post-hoc method for interpreting the predictions of GNNs which identifies unnecessary edges. We show that we can drop a large proportion of edges without deteriorating the performance of the model.
  arXiv  Detail & Related papers  (2020-10-01T17:51:19Z)
• Explanation-Guided Training for Cross-Domain Few-Shot Classification [96.12873073444091]
  Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. We introduce a novel training approach for existing FSC models. We show that explanation-guided training effectively improves the model generalization.
  arXiv  Detail & Related papers  (2020-07-17T07:28:08Z)
• Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning [134.77207192945053]
  Prior methods learn the neural-symbolic models using reinforcement learning approaches. We introduce the textbfgrammar model as a textitsymbolic prior to bridge neural perception and symbolic reasoning. We propose a novel textbfback-search algorithm which mimics the top-down human-like learning procedure to propagate the error.
  arXiv  Detail & Related papers  (2020-06-11T17:42:49Z)

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.