Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature as...
IEEE transactions on neural networks and learning systems
Dec 1, 2020
Residual connections significantly boost the performance of deep neural networks. However, few theoretical results address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks. This article st...
Neural networks : the official journal of the International Neural Network Society
May 1, 2020
Although our brain and deep neural networks (DNNs) can perform high-level sensory-perception tasks, such as image or speech recognition, the inner mechanism of these hierarchical information-processing systems is poorly understood in both neuroscienc...
Humans spontaneously organize a continuous experience into discrete events and use the learned structure of these events to generalize and organize memory. We introduce the (SEM) model of event cognition, which accounts for human abilities in event ...
Computational intelligence and neuroscience
Jan 1, 2020
Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In o...
Despite enormous progress in machine learning, artificial neural networks still lag behind brains in their ability to generalize to new situations. Given identical training data, differences in generalization are caused by many defining features of a...
We present a critical review of computational models of generalization of simple grammar-like rules, such as ABA and ABB. In particular, we focus on models attempting to account for the empirical results of Marcus et al. (Science, 283(5398), 77-80 19...
IEEE transactions on neural networks and learning systems
Oct 1, 2018
Online learning has been successfully applied in various machine learning problems. Conventional analysis of online learning achieves a sharp generalization bound with a strongly convex assumption. In this paper, we study the generalization ability o...
Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categ...
Transposition is a tendency for organisms to generalize relationships between stimuli in situations where training does not objectively reward relationships over absolute, static associations. Transposition has most commonly been explained as either ...