← Back to all news
arXiv Machine Learning By Alex Chan, Shafi Muhtasim Chowdhury, Ekin Can Erku\c{s}, Ole-Christoffer Granmo, Alex Yakovlev, Rishad Shafik

CRISP: A Framework for Clause-Reconstructed Interpretable NeuroSymbolic Propositions

Read the original on arXiv Machine Learning →

CRISP is a framework that reconstructs the last‑layer activation vector of binary neural teachers into Tsetlin Machine clauses, enabling a symbolic trace from input thresholds to teacher neurons. It sign‑binarizes penultimate activations and assigns an Individual TM to each neuron, representing each hidden bit with propositional clauses over Booleanized inputs. Experiments on MNIST, KMNIST, FashionMNIST, SVHN, and CIFAR‑10 show that this binarization preserves teacher accuracy while the main limitation is reconstruction error, with quartile one‑bit Booleanization achieving the best fidelity on SVHN.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Sep 15

Certifiably Interpretable Training of ReLU-MLPs for Boolean Tasks with Guaranteed Truth-Table Generalization

The paper introduces MACCHIATO, a training algorithm that builds a ReLU‑MLP from partial truth‑table data while simultaneously constructing an explicit Boolean circuit over AND, OR, and XOR gates that certifies the network’s computation. The method iteratively projects residuals onto low‑dimensional Boolean classes, compiles the resulting circuit into a ReLU‑MLP, and uses logic minimization and influence‑based variable selection to achieve a six‑layer network with provable truth‑table error bounds. Experiments on synthetic random‑junta tasks show that these certified networks outperform Adam‑trained MLPs in data‑sparse or projection‑aligned regimes and complete faster than flat ESPRESSO in certain settings.

By Hrad Ghoukasian, Anastasis Kratsios
safety
More like this →
arXiv Computation and Language
Oct 19

Automatic Tensor-level Execution of Abstract Interpreters for Neural Networks

Neuton is a framework that automatically converts neuron‑level abstract interpreter specifications into tensor‑level executables, enabling the creation of new DNN certifiers without manual tensor programming. It introduces a stack‑based intermediate representation with shape analysis, domain‑specific rewrite rules for efficient tensor operations, and a novel sparse tensor format (gBCSR) tailored to certifier sparsity. The authors demonstrate that Neuton preserves neuron‑level semantics and evaluate it on eight certifiers across 32 DNN benchmarks, compiling four existing certifiers and producing executable implementations for four others, revealing new precision‑runtime trade‑offs such as SkipPoly matching DeepPoly’s precision while often running faster.

By Avaljot Singh, Yasmin Chandini Sarita, Aditya Mishra, Gagandeep Singh, Charith Mendis
efficiencybenchmarks
More like this →