Tootfinder

{\L}ukasiewicz Logic with Actions for Neural Networks training
Ioana Leu\c{s}tean (University of Bucharest), Bogdan Macovei (University of Bucharest)
https://arxiv.org/abs/2509.13020

Łukasiewicz Logic with Actions for Neural Networks training
Based on the already known connection between multilayer perceptrons and Lukasiewicz logic with rational coefficients, we take a step forward in analyzing its training process using a three-sorted hybrid modal logic: a multilayer perceptron is a logical formula; the actions of the training process are modal operators; the training process is a sequence of logical deductions. Using the proof assistant and the programming language Lean 4, the algorithmic implementation of the training process is …

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Nathan He, Cody Chen
https://arxiv.org/abs/2509.12287 https://arxi…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaChe…

Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
Aueaphum Aueawatthanaphisut, Nyi Wunna Tun
https://arxiv.org/abs/2510.06010

Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
The comparative evaluation between classical and quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a benchmark control environment. The study employed a multilayer perceptron (MLP) agent as a classical baseline and a parameterized variational quantum circuit (VQC) as a quantum counterpart, both trained on the CartPole-v1 environment over 500 episodes. Empirical results dem…

On the sensitivity of different galaxy properties to warm dark matter
Bel\'en Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
https://arxiv.org/abs/2510.05037

On the sensitivity of different galaxy properties to warm dark matter
We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We begin by using a Multilayer Perceptron (MLP) coupled with a normalizing flow to explore global statistical descriptors of galaxy populations, such as the mean, standard deviation, and histograms of 14 galaxy properties. We find that subhalo gas mass is the most informative feature for constraining the WDM mass, achievin…

(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Luca Bergamin, Roberto Confalonieri, Fabio Aiolli
https://arxiv.org/abs/2509.22384

(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Deep neural networks are widely used in practical applications of AI, however, their inner structure and complexity made them generally not easily interpretable. Model transparency and interpretability are key requirements for multiple scenarios where high performance is not enough to adopt the proposed solution. In this work, a differentiable approximation of $L_0$ regularization is adapted into a logic-based neural network, the Multi-layer Logical Perceptron (MLLP), to study its efficacy in r…

Efficient Online Large-Margin Classification via Dual Certificates
Nam Ho-Nguyen, Fatma K{\i}l{\i}n\c{c}-Karzan, Ellie Nguyen, Lingqing Shen
https://arxiv.org/abs/2509.19670 htt…

Efficient Online Large-Margin Classification via Dual Certificates
Online classification is a central problem in optimization, statistical learning and data science. Classical algorithms such as the perceptron offer efficient updates and finite mistake guarantees on linearly separable data, but they do not exploit the underlying geometric structure of the classification problem. We study the offline maximum margin problem through its dual formulation and use the resulting geometric insights to design a principled and efficient algorithm for the online setting.…

On the solution of Euclidean path integrals with neural networks
Gabor Balassa
https://arxiv.org/abs/2509.16953 https://arxiv.org/pdf/2509.16953

On the solution of Euclidean path integrals with neural networks
This paper proposes a numerical method using neural networks to solve the path integral problem in quantum mechanics for arbitrary potentials. The method is based on a radial basis function expansion of the interaction term that appears in the Euclidean path integral formalism. By constructing a corresponding multi-layered perceptron-type neural network with exponential nonlinearities in the hidden layer, the original path integral can be approximated by a linear combination of Gaussian path in…