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arXiv AI

Predicate Neuron: Differentiable Quantization of Predicate Relations in Gradient-based Learning

The paper introduces the Native Predicate Neuron, a differentiable logic‑reasoning module that can be inserted into standard neural architectures. It learns to alternate between logical operations using a single trainable parameter and employs a dual‑nature gradient mechanism that performs hard logical operations during training while remaining differentiable for backpropagation. Experiments on Fully‑Connected, Convolutional, Recurrent, and Transformer models show that adding Predicate Neurons speeds convergence, improves accuracy, and reduces loss, especially on tasks involving monotonic reasoning or discontinuities.

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arXiv Machine Learning
Sep 15

Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks

The paper introduces a flexible symbolic framework that efficiently computes logical explanations for deep neural networks by parameterizing explanations with internal neuron activations and leveraging general-purpose logical engines like SMT solvers. Unlike previous methods that rely on specialized verifiers or are limited to individual input features, this approach is not restricted in shape and can scale to deep architectures. Experiments on image recognition and medical benchmarks demonstrate improved computational efficiency and the ability to explain networks that were previously intractable for logic-based methods.

By Tom\'a\v{s} Kol\'arik, Faezeh Labbaf, Fabrizio Leopardi, Grigory Fedyukovich, Michael Wand, Natasha Sharygina
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arXiv AI
Jul 24

Differentiable Logic Programming to Mitigate Reasoning Shortcuts in Neurosymbolic Systems

arXiv:2607. 21185v1 Announce Type: new Abstract: Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability, but recent work has shown they are susceptible to shortcut reasoning behaviors.

By Akihiro Takemura (National Institute of Informatics, Tokyo, Japan), Katsumi Inoue (National Institute of Informatics, Tokyo, Japan)
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arXiv AI
Oct 9

The Polytopal Neural Network

The paper introduces Polytopal Neural Networks (PNNs), a framework that enforces a polytope-based structure on neural network representations to extract distinct layer-wise aspects. By using learned corpus representations and an amortized simplex inference procedure, PNNs preserve meaningful latent structures with minimal performance loss and yield compressed representations comparable to vector‑quantized (VQ) methods. The authors demonstrate that PNNs provide a new, interpretable approach to VQ deep learning training while maintaining competitive performance.

By A. Emilie J. Wedenborg, Anders V. N{\o}rskov, Teresa Dorszewski, Kristoffer Wickstr{\o}m, Morten M{\o}rup
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Hugging Face Trending Papers
Jul 9

FPGN: Redefining Ultra-Fast Programmable Gate-based Neural Acceleration with Differentiable LUTs

Achieving nanosecond-scale inference latency for deep neural networks (DNNs) has become a primary architectural concern for latency-critical applications. While Field-Programmable Gate Arrays (FPGAs) offer a promising substrate for low-latency inference, conventional FPGA accelerators remain arithmetic-centric, using LUTs primarily as building blocks for numerical operators and peripheral logic.

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