Metalearning and neuromodulation
K Doya - Neural networks, 2002 - Elsevier
This paper presents a computational theory on the roles of the ascending neuromodulatory
systems from the viewpoint that they mediate the global signals that regulate the distributed …
systems from the viewpoint that they mediate the global signals that regulate the distributed …
[HTML][HTML] Serotonin neurons modulate learning rate through uncertainty
Regulating how fast to learn is critical for flexible behavior. Learning about the
consequences of actions should be slow in stable environments, but accelerate when that …
consequences of actions should be slow in stable environments, but accelerate when that …
[PDF][PDF] Covariate shift adaptation by importance weighted cross validation.
A common assumption in supervised learning is that the input points in the training set follow
the same probability distribution as the input points that will be given in the future test phase …
the same probability distribution as the input points that will be given in the future test phase …
[PDF][PDF] Incremental support vector learning: Analysis, implementation and applications.
Abstract Incremental Support Vector Machines (SVM) are instrumental in practical
applications of online learning. This work focuses on the design and analysis of efficient …
applications of online learning. This work focuses on the design and analysis of efficient …
Real-time adaptive EEG source separation using online recursive independent component analysis
Independent component analysis (ICA) has been widely applied to electroencephalographic
(EEG) biosignal processing and brain-computer interfaces. The practical use of ICA …
(EEG) biosignal processing and brain-computer interfaces. The practical use of ICA …
On the Parzen kernel-based probability density function learning procedures over time-varying streaming data with applications to pattern classification
In this paper, we propose a recursive variant of the Parzen kernel density estimator (KDE) to
track changes of dynamic density over data streams in a nonstationary environment. In …
track changes of dynamic density over data streams in a nonstationary environment. In …
Separation of stationary and non-stationary sources with a generalized eigenvalue problem
Non-stationary effects are ubiquitous in real world data. In many settings, the observed
signals are a mixture of underlying stationary and non-stationary sources that cannot be …
signals are a mixture of underlying stationary and non-stationary sources that cannot be …
Deep metric learning framework combined with Gramian angular difference field image generation for Raman spectra classification based on a handheld Raman …
Y Cai, Z Yao, X Cheng, Y He, S Li, J Pan - Spectrochimica Acta Part A …, 2023 - Elsevier
Rapid identification of unknown material samples using portable or handheld Raman
spectroscopy detection equipment is becoming a common analytical tool. However, the …
spectroscopy detection equipment is becoming a common analytical tool. However, the …
Online sequential reduced kernel extreme learning machine
In this paper, we present an Online Sequential Reduced Kernel Extreme Learning Machine
(OS-RKELM). In OS-RKELM, only a small part of the instances in the original training …
(OS-RKELM). In OS-RKELM, only a small part of the instances in the original training …
[HTML][HTML] Stochastic Control for Bayesian Neural Network Training
In this paper, we propose to leverage the Bayesian uncertainty information encoded in
parameter distributions to inform the learning procedure for Bayesian models. We derive a …
parameter distributions to inform the learning procedure for Bayesian models. We derive a …