Add to ORKGGeneralization is at the core of machine learning models. However, the definition of generalization is not entirely clear. We employ set theory to introduce the concepts of algorithms, hypotheses, and dataset generalization. We analyze the properties of dataset generalization and prove a theorem on surrogate generalization procedures. This theorem leads to our generalization method. Through a generalization experiment on the MNIST dataset, we obtain 13,541 sample bases. When we use the entire training set to evaluate the model's performance, the models achieve an accuracy of 99.945%. However, if we shift the sample bases or modify the neural network structure, the performance experiences a significant decline. We also identify consistently ...
Defining and accurately measuring generalization in generative models remains an ongoing challenge a...
Machine learning models that can generalize to unseen domains are essential when applied in real-wor...
The great success of deep learning heavily relies on increasingly larger training data, which comes ...
A machine learning (ML) system must learn not only to match the output of a target function on a tra...
With a direct analysis of neural networks, this paper presents a mathematically tight generalization...
This paper provides theoretical insights into why and how deep learning can generalize well, despite...
Machine learning systems generally assume that the training and testing distributions are the same. ...
This survey includes systematic generalization and a history of how machine learning addresses it. W...
During the past decade, machine learning techniques have achieved impressive results in a number of ...
Generalization is a central aspect of learning theory. Here, we propose a framework that explores an...
Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary ...
Machine learning models are typically configured by minimizing the training error over a given train...
Machine learning models are typically configured by minimizing the training error over a given train...
The generalization mystery in deep learning is the following: Why do over-parameterized neural netwo...
This thesis presents a new theory of generalization in neural network types of learning machines. Th...
Defining and accurately measuring generalization in generative models remains an ongoing challenge a...
Machine learning models that can generalize to unseen domains are essential when applied in real-wor...
The great success of deep learning heavily relies on increasingly larger training data, which comes ...
A machine learning (ML) system must learn not only to match the output of a target function on a tra...
With a direct analysis of neural networks, this paper presents a mathematically tight generalization...
This paper provides theoretical insights into why and how deep learning can generalize well, despite...
Machine learning systems generally assume that the training and testing distributions are the same. ...
This survey includes systematic generalization and a history of how machine learning addresses it. W...
During the past decade, machine learning techniques have achieved impressive results in a number of ...
Generalization is a central aspect of learning theory. Here, we propose a framework that explores an...
Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary ...
Machine learning models are typically configured by minimizing the training error over a given train...
Machine learning models are typically configured by minimizing the training error over a given train...
The generalization mystery in deep learning is the following: Why do over-parameterized neural netwo...
This thesis presents a new theory of generalization in neural network types of learning machines. Th...
Defining and accurately measuring generalization in generative models remains an ongoing challenge a...
Machine learning models that can generalize to unseen domains are essential when applied in real-wor...
The great success of deep learning heavily relies on increasingly larger training data, which comes ...