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Does Symbolic Knowledge Prevent Adversarial Fooling?

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Stefano Teso

Machine Learning · Submitted 19 Dec 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Focusing on deep probabilistic (logical) graphical models -- i.e., constrained joint distributions whose parameters are determined (in part) by neural nets based on low-level inputs -- we draw attention to an elementary but unintended consequence of symbolic knowledge: that the resulting constraints can propagate the negative effects of adversarial examples.

how it is stated · asserted 0.94 read the record →

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arXiv ID1912.10834 (version 1)
Pith IDQL2UT7C2, Pith's permanent record of this paper and its review
CitationStefano Teso. "Does Symbolic Knowledge Prevent Adversarial Fooling?." arXiv:1912.10834 (2019). https://pith.science/paper/QL2UT7C2
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The compiled record

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,957 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.67 · human participants 0.03 · controlled comparison 0.07 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.11 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Arguments in favor of injecting symbolic knowledge into neural architectures abound. When done right, constraining a sub-symbolic model can substantially improve its performance and sample complexity and prevent it from predicting invalid configurations. Focusing on deep probabilistic (logical) graphical models -- i.e., constrained joint distributions whose parameters are determined (in part) by neural nets based on low-level inputs -- we draw attention to an elementary but unintended consequence of symbolic knowledge: that the resulting constraints can propagate the negative effects of adversarial examples.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Arguments in favor of injecting symbolic knowledge into neural architectures abound. background 1.00 0.04 0.02 asserted 0.84
s1 When done right, constraining a sub-symbolic model can substantially improve its performance and sample complexity and prevent it from predicting invalid configurations. background 1.00 0.07 0.05 asserted 0.75
s2 Focusing on deep probabilistic (logical) graphical models -- i.e., constrained joint distributions whose parameters are determined (in part) by neural nets based on low-level inputs -- we draw attention to an elementary but unintended consequence of symbolic knowledge: that the resulting constraints can propagate the negative effects of adversarial examples. result 0.49 0.48 0.89 asserted 0.94

What kind of paper the classifier reports this is: position or commentary 0.96 · evidence from argument 0.99.

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Arguments in favor of injecting symbolic knowledge into neural architectures abound. When done right, constraining a sub-symbolic model can substantially improve its performance and sample complexity and prevent it from predicting invalid configurations. Focusing on deep probabilistic (logical) graphical models -- i.e., constrained joint distributions whose parameters are determined (in part) by neural nets based on low-level inputs -- we draw attention to an elementary but unintended consequence of symbolic knowledge: that the resulting constraints can propagate the negative effects of adversarial examples.

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1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 2 sentences without a confident label

Cites as background 2 papers
  • It is well known that neural networks are often susceptible to rather eye-catching adversarial perturbations that can alter their output by arbitrary amounts [ref] .

    Intriguing properties of neural networks · Adversarial Examples and Constraints · 0.88

  • Prominent examples of dPGMs include DeepProbLog [ref] and “neural” extensions of Markov Logic [ref] .

    Neural Markov Logic Networks · Introduction · 0.96

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