论文梳理关系图：Neural Symbolic and Probabilistic Logic Papers

2022

Neuro-Symbolic Approaches in Artificial Intelligence

National Science Review

Paper

A perspective paper that provide a rough guide to key research directions, and literature pointers for anybody interested in learning more about neural-symbolic learning.

2022

A review of some techniques for inclusion of domain-knowledge into deep neural networks

Nature Scientific Reports

Paper

Presents a survey of techniques for constructing deep networks from data and domain-knowledge. It categorises these techniques into 3 major categories: (1) changes to input representation, (2) changes to loss function, (3a) changes to model structure and (3b) changes to model parameters.

2021

Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs

AI Open

Paper

Take a thorough look at the development of the symbolic, neural and hybrid reasoning on knowledge graphs.

2021

Modular design patterns for hybrid learning and reasoning systems

arXiv

Paper

Analyse a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems.

2021

How to Tell Deep Neural Networks What We Know

arXiv

Paper

This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks.

2020

From Statistical Relational to Neuro-Symbolic Artificial Intelligence

IJCAI

Paper

This survey identifies several parallels across seven different dimensions between these two fields.

2020

Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains

SUM

Paper

Survey work that provides further evidence for the connections between logic and learning.

2020

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

IJCAI

Paper

A Survey on Neural-Symbolic with GNN.

2020

Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey

Frontiers in Robotics and AI

Paper

In this paper we make a survey that aims to renew the link between symbolic representations and distributed/distributional representations.

2020

On the Binding Problem in Artificial Neural Networks

arXiv

Paper

In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information that is distributed throughout the network.

2019

Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning

Journal of Applied Logic

Paper

We survey recent accomplishments of neural-symbolic computing as a principled methodology for integrated machine learning and reasoning.

2017

Neural-Symbolic Learning and Reasoning: A Survey and Interpretation

arXiv

Paper

Reviews personal ideas and views of several researchers on neural-symbolic learning and reasoning.

2011

Statistical Relational AI: Logic, Probability and Computation

ICLP

Paper

We overview the foundations of StarAI.

2021

Meta Module Network for Compositional Visual Reasoning

WACV

Paper

Code

N2NMN application

2020

Neural Module Networks for Reasoning over Text

ICLR

Paper

Code

TMN, parser-NMN application

2020

Learning to Discretely Compose Reasoning Module Networks for Video Captioning

arXiv

Paper

Code

RMN, N2NMN application

2020

LRTA: A Transparent Neural-symbolic Reasoning Framework with Modular Supervision for VQA

arXiv

Paper

N2NMN application

2019

Self-Assembling Modular Networks for Interpretable Multi-hop Reasoning

arXiv

Paper

Code

N2NMN application

2019

Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering

ICML

Paper

Code

The author proposed ProbNMN, using variational method to generate reasoning graph.

2019

Explainable and Explicit Visual Reasoning over Scene Graphs

CVPR

Paper

Code

XNM, N2NMN + scene graph

2019

Learning to Assemble Neural Module Tree Networks for Visual Grounding

ICCV

Paper

Code

NMTree, parser-NMN application

2019

Structure Learning for Neural Module Networks

EACL

Paper

LNMN, follows Stack-NMN to add learnable (soft) modules

2018

Explainable Neural Computation via Stack Neural Module Networks

ECCV

Paper

Code

Stack-NMN, N2NMN + differentiable memory stack + soft program execution

2018

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

arXiv

Paper

Code

NS-VQA, N2NMN + scene graph

2018

Compositional Models for VQA: Can Neural Module Networks Really Count?

BICA

Paper

interesting (negative) result of N2NMN

2018

Transparency by Design: Closing the Gap between Performance and Interpretability in Visual Reasoning

CVPR

Paper

Code

TbD, soft modules / structures

2018

Visual Question Reasoning on General Dependency Tree

CVPR

Paper

Code

ACMN, parser-NMN (DPT -> structure)

2017

Learning to Reason: End-To-End Module Networks for Visual Question Answering

ICCV

Paper

Code

N2NMN

2017

Inferring and Executing Programs for Visual Reasoning

ICCV

Paper

Code

Basically N2NMN which refers N2NMN as "concurrent work"

2016

Learning to Compose Neural Networks for Question Answering

NAACL

Paper

Code

Compared to original NMN, the authors add a layout selector to select layout from several proposed candidates.

2016

Neural Module Networks

CVPR

Paper

Code

Initial paper. The authors proposed Neural Module Networks in this paper.

2021

Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images

ICCV

Paper

Code

we introduce an executor with learnable concept embedding magnitudes for handling distribution imbalance, and an operation calibrator for highlighting important operations and suppressing redundant ones

2019

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

ICLR

Paper

Code

Neuro-Symbolic Concept Learner in VQA

2017

β-VAE: Learning Basiz Visual Concept With A Constrained Variational Framework

ICLR

Paper

Automated discovery of interpretable factorised latent representations from raw image

2020

Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"

PMLR

Paper

Code

a Differentiable First-Order Logic formalism for VQA

2019

Learning by Abstraction: The Neural State Machine

NeurIPS

Paper

Given an image, we first predict a probabilistic graph then perform sequential reasoning over the graph.

2020

A Constraint-Based Approach to Learning and Explanation

AAAI

Paper

Code

Learning First Order Constraints

2018

A Semantic Loss Function for Deep Learning with Symbolic Knowledge

ICML

Paper

Code

Semantic Loss, a continuous regularizer of logic prior.

2017

Logic tensor networks for semantic image interpretation.

IJCAI

Paper

Code

Logic Tensor Networks (LTNs) are an SRL framework which integrates neural networks with first-order fuzzy logic.

2017

Semantic-based regularization for learning and inference

Artificial Intelligence

Paper

A Regularizer using fuzzy logic.

2016

Harnessing Deep Neural Networks with Logic Rules

ACL

Paper

We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules.

2021

Acquisition of Chess Knowledge in AlphaZero

arXiv

Paper

In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as it trains on the game of chess.

2021

Knowledge Neurons in Pretrained Transformers

arXiv

Paper

We explore how implicit knowledge is stored in pretrained Transformers by introducing the concept of knowledge neurons.

2019

Logical Explanations for Deep Relational Machines Using Relevance Information

JMLR

Paper

This work provides a methodology to generate symbolic explanations for predictions made by a deep neural network constructed from relational data, called DRMs. It investigates the use of a Bayes-like approach to identify logical proxies for local predictions of a DRM.

2022

Composition of Relational Features with an Application to Explaining Black-Box Predictors

arXiv

Paper

Code

Complex (deep) neural networks can be constructed from relational description of data using relational features. The input layer of the DNN are simple relational features (clauses) and further layers are formed by composing these features. The resulting DNN is called a Compositional Relational Machines (CRM), which is inherently explainable.

2021

Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment

Machine Learning Journal

Paper

Code

Constructing GNNs from relational data and symbolic domain-knowledge, via construction of "Bottom-Graphs"

2021

Incorporating symbolic domain knowledge into graph neural networks

Machine Learning Journal

Paper

Code

Constructing GNNs from relational data and symbolic domain-knowledge, via "Vertex Enrichment"

2020

Logical Neural Networks

NeurIPS

Paper

Transform a logic formula to NN-like. Relax Boolean to [0,1]

2019

Synthesizing datalog programs using numerical relaxation.

IJCAI

Paper

Code

Differential Datalog

2019

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

ICML

Paper

Code

Differential SAT

2018

Large-Scale Assessment of Deep Relational Machines

ILP

Paper

Constructs MLPs from relational data and symbolic domain-knowledge using "Propositionalisation"

2018

Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures

JAIR

Paper

Code

Creating deep neural networks from "templates" constructed from first-order logic rules.

2018

Learning Explanatory Rules from Noisy Data

JAIR

Paper

Code

Differentiable ILP

2017

TensorLog: Deep Learning Meets Probabilistic Databases

arXiv

Paper

Code

Relax Boolean truth value to [0,1]

2017

Differentiable Learning of Logical Rules for Knowledge Base Reasoning

NeurIPS

Paper

Code

Neural Logic Programming, learning probabilistic first-order logical rules for knowledge base reasoning in end-to-end model.

2017

End-to-end Differentiable Proving

NeurIPS

Paper

Code

We replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel.

2021

Neural Markov Logic Networks

UAI

Paper

Code

NMLNs are an exponential-family model for modelling distributions over possible worlds without explicit logic rules.

2020

NeurASP: Embracing Neural Networks into Answer Set Programming

IJCAI

Paper

Code

NeurASP, a simple extension of answer set programs by embracing neural networks.

2019

Neural Logic Machines

ICLR

Paper

Code

Logic predicates as tensors, logic rules as neural operators.

2019

DeepLogic: Towards End-to-End Differentiable Logical Reasoning

AAAI-MAKE

Paper

Code

Feed logic rules into RNN as a string

2018

DeepProbLog: Neural Probabilistic Logic Programming

NeurIPS

Paper

Code

Add "neural predicates" to ProbLog which is a probabilistic logic programming language.

2021

Neural-Symbolic Integration: A Compositional Perspective

AAAI

Paper

Treating Neural and Symbolic as black boxes to be integrated, without making assumptions on their internal structure and semantics.

2020

Relational Neural Machines

ECAI

Paper

Relational Neural Machines, a novel framework allowing to jointly train the parameters of the learners and of a First–Order Logic based reasoner.

2020

Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning

ICML

Paper

Code

NGS, (1) introducing the grammar model as a symbolic prior, (2) proposing a novel back-search algorithm to propagate the error through the symbolic reasoning module efficiently.

2019

NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language

ACL

Paper

A Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders.

2018

Lifted relational neural networks: Efficient learning of latent relational structures.

JAIR

Paper

Combine the interpretability and expressive power of first order logic with the effectiveness of neural network learning.

2007

ProbLog: A Probabilistic Prolog and its Application in Link Discovery

IJCAI

Paper

Code

ProbLog, a library for probabilistic logic programming.

2005

Learning the structure of Markov logic networks

ICML

Paper

an algorithm for learning the structure of MLNs from relational databases

2001

Bayesian Logic Programs

Paper

Bayesian networks + Logic Program

2001

Parameter Learning of Logic Programs for Symbolic-statistical Modeling

JAIR

Paper

Wepropose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution.

1996

Stochastic Logic Programs

Advances in ILP

Paper

A formulaton: Stochastic Logic Programs

1992

Probabilistic logic programming

Information and Computation

Paper

A formulation of Probabilistic logic programming.

2008

Event Modeling and Recognition Using Markov Logic Networks

ECCV

Paper

Application of MLNs

2008

Hybrid Markov Logic Networks

AAAI

Paper

Extend Markov Logic Networks to continous space.

2007

Efficient Weight Learning for Markov Logic Networks

PKDD

Paper

weights learning of MLNs

2005

Discriminative Training of Markov Logic Networks

AAAI

Paper

a discriminative approach to training MLNs

2005

Markov Logic Networks

Springer

Paper

Combining Logic and Markov Networks, a classic paper.

2022

Composition of Relational Features with an Application to Explaining Black-Box Predictors

arXiv

Paper

Complex (deep) neural networks can be constructed from relational description of data using relational features. The input layer of the DNN are simple relational features (clauses) and further layers are formed by composing these features. The resulting DNN is called a Compositional Relational Machines (CRM), which is inherently explainable.

2021

Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment

Machine Learning Journal

Paper

Constructing GNNs from relational data and symbolic domain-knowledge, via construction of "Bottom-Graphs"

2019

Logical Explanations for Deep Relational Machines Using Relevance Information

JMLR

Paper

Our interest in this paper is in the construction of symbolic explanations for predictions made by a deep neural network on DRM

2018

Exact Learning of Lightweight Description Logic Ontologies

JMLR

Paper

We study the problem of learning description logic (DL) ontologies in Angluin et al.’s framework of exact learning via queries.

2017

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

JMLR

Paper

In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data.

2017

Answering FAQs in CSPs, Probabilistic Graphical Models, Databases, Logic and Matrix Operations (Invited Talk)

STOC

Paper

A invited talk on a general framework

2020

Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing

ICML

Paper

Logic Rules + GNN + RL

2020

WHAT CAN NEURAL NETWORKS REASON ABOUT?

ICLR

Paper

How NN structured correlates with the performance on different reasoning tasks.

2019

Bridging Machine Learning and Logical Reasoning by Abductive Learning

NeurIPS

Paper

machine learning model learns to perceive primitive logic facts from data, while logical reasoning can exploit symbolic domain knowledge and correct the wrongly perceived facts for improving the machine learning models

2013

Deep relational machines

NeurIPS

Paper

A DRM learns the first layer of representation by inducing first order Horn clauses and the successive layers are generated by utilizing restricted Boltzmann machines.

2021

Off-Policy Differentiable Logic Reinforcement Learning

ECML PKDD

Paper

In this paper, we proposed an Off-Policy Differentiable Logic Reinforcement Learning (OPDLRL) framework to inherit the benefits of interpretability and generalization ability in Differentiable Inductive Logic

2020

Exploring Logic Optimizations with Reinforcement Learning and Graph Convolutional Network

MLCAD

Paper

Code

We propose a Markov decision process (MDP) formulation of the logic synthesis problem and a reinforcement learning (RL) algorithm incorporating with graph convolutional network to explore the solution search space.

2020

Reinforcement Learning with External Knowledge by using Logical Neural Networks

IJCAI Workshop

Paper

We propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.

2019

Transfer of Temporal Logic Formulas in Reinforcement Learning

IJCAI

Paper

We first propose an inference technique to extract metric interval temporal logic (MITL) formulas in sequential disjunctive normal form from labeled trajectories collected in RL of the two tasks.

2019

Neural Logic Reinforcement Learning

ICML

Paper

Code

We propose a novel algorithm named Neural Logic Reinforcement Learning (NLRL) to represent the policies in reinforcement learning by first-order logic.

2020

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

ICLR

Paper

Code

CFQ, a large dataset of Natural Language Question Answering

2021

Domiknows: A library for integration of symbolic domain knowledge in deep learning

arXiv

Homepage

Code

This library provides a language interface integrate Domain Knowldge in Deep Learning.

2019

LYRICS: a General Interface Layer to Integrate Logic Inference and Deep Learning

ECML

Paper

Tensorflow, seems only in design, not implemented

2007

ProbLog: A Probabilistic Prolog and its Application in Link Discovery

IJCAI

Paper

Code

ProbLog, a library for probabilistic logic programming.