Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling

On the Simple Inverse Sampling with Replacement

In this paper we derive some unbiased estimators of the population mean under simple inverse sampling with replacement, using the class of Hansen-Hurwitz and Horvitz-Thompson type estimators and the post-stratification approach. We also compare the efficiency of resulting estimators together with Murthy's estimator. We show that in despite of general belief, the strategy consisting of inverse s...

Accelerating Microarchitectural Simulation via Statistical Sampling Principles

Net2Net: Accelerating Learning via Knowledge Transfer

We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a significantly larger neural net. During real-world workflows, one often trains very many different neural networks during the experimentation and design process. This is a wasteful process in which each new model is trained from s...

Interval Estimation of Bounded Variable Means via Inverse Sampling ∗

It is a ubiquitous problem to estimate the means of bounded random variables. Specially, the problem of estimating the probability of an event can be formulated as the estimation of the mean of a Bernoulli variable. Moreover, in many applications, one needs to estimate a quantity μ which can be bounded in [0, 1] after proper operations of scaling and translation. A typical approach is to design...

Combining Intelligent Methods for Learner Modelling in Exploratory Learning Environments

Most of the existing learning environments work in wellstructured domains by making use of or combining AI techniques in order to create and update a learner model, provide individual and/or collaboration support and perform learner diagnosis. In this paper we present an approach that exploits the synergy of case-base reasoning and soft-computing for learner modelling in an ill-structured domai...