Add to ORKGThis survey includes systematic generalization and a history of how machine learning addresses it. We aim to summarize and organize the related information of both conventional and recent improvements. We first look at the definition of systematic generalization, then introduce Classicist and Connectionist. We then discuss different types of Connectionists and how they approach the generalization. Two crucial problems of variable binding and causality are discussed. We look into systematic generalization in language, vision, and VQA fields. Recent improvements from different aspects are discussed. Systematic generalization has a long history in artificial intelligence. We could cover only a small portion of many contributions. We hope this ...
Generalization is a fundamental operation of inductive inference. While first order syntactic genera...
This paper provides theoretical insights into why and how deep learning can generalize well, despite...
Generalization is a central aspect of learning theory. Here, we propose a framework that explores an...
The ability to generalize well is one of the primary desiderata for models of natural language proce...
The ability to generalise well is one of the primary desiderata of natural language processing (NLP)...
Machine learning systems generally assume that the training and testing distributions are the same. ...
Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary ...
The meaning of the word generalization is so general that we can nd its occurrences in almost every ...
Generalization is at the core of machine learning models. However, the definition of generalization ...
A machine learning (ML) system must learn not only to match the output of a target function on a tra...
My 1971 Turing Award Lecture was entitled ``Generality in Artificial Intelligence''. The topic turne...
Experiments in Artificial Language Learn- ing have revealed much about the cogni- tive mechanisms un...
With a direct analysis of neural networks, this paper presents a mathematically tight generalization...
This thesis presents a new theory of generalization in neural network types of learning machines. Th...
Generalization is a critical aspect of doing mathematics, with policy makers recommending that it be...
Generalization is a fundamental operation of inductive inference. While first order syntactic genera...
This paper provides theoretical insights into why and how deep learning can generalize well, despite...
Generalization is a central aspect of learning theory. Here, we propose a framework that explores an...
The ability to generalize well is one of the primary desiderata for models of natural language proce...
The ability to generalise well is one of the primary desiderata of natural language processing (NLP)...
Machine learning systems generally assume that the training and testing distributions are the same. ...
Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary ...
The meaning of the word generalization is so general that we can nd its occurrences in almost every ...
Generalization is at the core of machine learning models. However, the definition of generalization ...
A machine learning (ML) system must learn not only to match the output of a target function on a tra...
My 1971 Turing Award Lecture was entitled ``Generality in Artificial Intelligence''. The topic turne...
Experiments in Artificial Language Learn- ing have revealed much about the cogni- tive mechanisms un...
With a direct analysis of neural networks, this paper presents a mathematically tight generalization...
This thesis presents a new theory of generalization in neural network types of learning machines. Th...
Generalization is a critical aspect of doing mathematics, with policy makers recommending that it be...
Generalization is a fundamental operation of inductive inference. While first order syntactic genera...
This paper provides theoretical insights into why and how deep learning can generalize well, despite...
Generalization is a central aspect of learning theory. Here, we propose a framework that explores an...