… learn a single target function (one row), our model learns to approximate instantaneously an
infinity of them. Meta-learning functional regression In this work, we meta-learn a … function O …

… If P is a family of probability distributions, we define a P-measure of impossibility to be a
function which is a P-measure of impossibility for all Pe P. Most of all we are interested in the cm(…

… , the complex signal transduction mechanisms of LTD have … transduction mechanisms
provides new tools for investigating functional roles of LTD in various forms of cerebellar learning…

… We consider the problem of learning as a problem of estimating a function given a set of
examples (vectors) (x 1 , y 1 ), …, (x T , y T ), where x t is a vector of attributes and y t is a label. …

… Transductive Transfer Learning denotes the transfer learning … ’s original definition of “transduction”,
current terminology in … term “transduction” has been misused in the transfer learning …

… on this field of research, named graph transduction, we refer to [30]. Formally … function
assigning a non-negative similarity value to each edge ϵ ∈ E. The task of transduction learning is …

… of learning and memory. The progress in the understanding of the glutamate receptors and
related signal transduction systems in learning … and functional characteristics. In a variety of …

… learning algorithm, called Query-by-Transduction (QBT), based on p-values obtained
from a transductive learning … a number of reference points for function approximation (regression) …

… transduction in the teaching and learning of science. We start out by discussing the function
… We then go on to illustrate the role that transduction can play in the teaching and learning of …

… a shape distance function, which does not need to be a metric, we learn a new distance
function that is expressed by … We introduce now a method to learn a new similarity function simT …