Branch prediction reversal has been proved to be an effective alternative approach to dropping misprediction rates by means of adding a Confidence Estimator to a correlating branch predictor. This paper presents a Branch Prediction Reversal Unit (BPRU) especially oriented to enhance correlating branch predictors, such as the gshare and the Alpha 21264 metapredictor. The novelty of this proposal...

Pervasive resp. ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user’s desires. By means of context prediction systems get ready for future human activities and can act proactively. Predictions, however, are never 100% correct. In case of unreliable prediction results it is sometimes better to ...

Information extraction systems automatically extract structured information from machine-readable documents, such as newswire, web, and multimedia. Despite significant improvement, the performance is far from perfect. Hence, it is useful to accurately estimate confidence in the correctness of the extracted information. Using the Knowledge Base Population Slot Filling task as a case study, we pr...

Automatic speech recognition (ASR) systems produce transcriptions for audio which sometimes contain errors. It is useful to know how much condence may be placed in this output being correct. Condence estimation is concerned with obtaining scores which quantify this level of condence. e development and application of a principled, žexible framework using conditional random eld (CRF) models f...

Confidence Estimation has been extensively used in Speech Recognition and now it is also being applied in Statistical Machine Translation. Its basic goal is to estimate a confidence measure for each word in a given hypothesis, in order to locate those words, if any, that are likely to be incorrectly recognised or translated. It can be seen as a two-class pattern recognition problem in which eac...

A confidence sequence (CS) is a of intervals that valid at arbitrary data-dependent stopping times. These are useful in applications like A/B testing, multi-armed bandits, off-policy evaluation, election auditing, etc. We present three approaches to constructing for the population mean, under minimal assumption only an upper bound $\sigma^2$ on variance known. While previous works rely light-ta...

We introduce confidence region techniques for analyzing and visualizing the performance of two-dimensional parametric shape estimators. Assuming an asymptotically normal and efficient estimator for a finite parameterization of the object boundary, Cramér-Rao bounds are used to define a confidence region, centered around the true boundary. Computation of the probability that an entire boundary e...

This note proposes a method, which can be applied to searches and more in general to any cross section measurement, to maximize the analysis sensitivity.

We describe an estimation technique which, given a measurement of the depth of a target from a wide-fieldof-view (WFOV) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. This work constitutes the first application to the computer vision domain of optimal fixed-size confidenceinterval decision theory. The approach is evaluated in terms of t...