Skip to main content

Table 1 Notations

From: CLeaR: An adaptive continual learning framework for regression tasks

Symbol

Definition

X

The N×M matrix with N feature samples

Y

The matrix with N measurements associated with X

\(\hat {Y}\)

The matrix with N predictions associated with X

xn

The nth column feature vector, \(\mathbf {x}_{n}^{\mathrm {T}}=X_{n,:}\)

yn

The nth measurement, yn=Yn,:

\(\hat {y}_{n}\)

The nth prediction, \(\hat {y}_{n} = \hat {Y}_{n,:}\)

D

The dataset, \(D=\{\left (\mathbf {x}_{n}, y_{n} \right) | n=1, \dots N \}\)

Θl

The weight matrix of the lth layer, \(\Theta _{l} \in \mathbb {R}^{{l-1} \times {l}}\)

\(\theta ^{i}_{l}\)

The ith element of Θl

fl

The activation function of the lth layer, \(f_{l}\colon \mathbb {R} \to \mathbb {R}\)

fΘ(·)

The neural network with given weight martrix Θ

zl

The output column vector of the lth layer

L

Loss function

P

Probability density

\(\mathcal {N}(\mu, \sigma ^{2})\)

Gaussian distribution with mean μ and variance σ2

Ft

The Fisher information matrix of the tth task

\(F_{t}^{i}\)

The ith diagonal element of Ft