NEUROSPF: A tool for the Symbolic Analysis of Neural Networks

Bayesian Inference by Symbolic Model Checking

This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on top of the Storm model checker, we show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference on large Bayesian network benchmarks. We compare our result to inference using probabilistic sentential decision diagrams and vtrees,…

Efficient Symbolic Reasoning for Neural-Network Verification

The neural network has become an integral part of modern software systems. However, they still suffer from various problems, in particular, vulnerability to adversarial attacks. In this work, we present a novel program reasoning framework for neural-network verification, which we refer to as symbolic reasoning. The key components of our framework are the use of the symbolic domain and the quadratic relation. The symbolic domain has very flexible semantics, and the quadratic relation is quite expressive. They allow us to encode many verification problems for neural networks as quadratic program…

TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators

Robots assisting us in factories or homes must learn to make use of objects as tools to perform tasks, e.g., a tray for carrying objects. We consider the problem of learning commonsense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. We introduce a novel neural model, termed TANGO, for predicting task-specific tool interactions, trained using demonstrations from human teachers instructing a virtual robot. TANGO encodes the world state, comprising objects and symbolic relationships between them, using…

引入热力学信息启发的符号回归——一种用于热力学状态方程开发的工具

Introducing Thermodynamics-Informed Symbolic Regression -- A Tool for Thermodynamic Equations of State Development

本文提出了热力学信息启发的符号回归工具TiSR，通过结合SR基础与处理分散实验数据及残差处理等扩展功能，旨在加速并改进热力学状态方程的开发过程。

通过通用神经符号回归学习可解释的网络动力学

Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression

本文提出一种通用神经符号回归计算工具，结合深度学习与预训练符号回归自动学习复杂网络动力学的符号变化模式，在多领域场景验证中展现卓越效率，并成功应用于真实系统。

An Approach to Symbolic Regression Using Feyn

In this article we introduce the supervised machine learning tool called Feyn. The simulation engine that powers this tool is called the QLattice. The QLattice is a supervised machine learning tool inspired by Richard Feynman's path integral formulation, that explores many potential models that solves a given problem. It formulates these models as graphs that can be interpreted as mathematical equations, allowing the user to completely decide on the trade-off between interpretability, complexity and model performance. We touch briefly upon the inner workings of the QLattice, and show how to ap…