Titlebook: Making the Invisible Visible; Understanding Leader Tojo Thatchenkery,Keimei Sugiyama Book 2011 Tojo Thatchenkery and Keimei Sugiyama 2011 A

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CANT

Tojo Thatchenkery,Keimei Sugiyamacan measure this .. That is, we want to be able to measure whether a result is interesting from a subjective point of view..With this as our goal, we formalise how to probabilistically model real-valued data by the Maximum Entropy principle, where we allow statistics on . sets of cells as background

TOXIC

Tojo Thatchenkery,Keimei Sugiyamat and is costly. Recent years have seen much progress in techniques for automated fault localization, specifically using program spectra – executions of failed and passed test runs provide a basis for isolating the faults. Despite the progress, fault localization in large programs remains a challeng

不合

Tojo Thatchenkery,Keimei Sugiyamanal costs challenge their applicability to resource-constrained environments. Taming computational costs has hitherto focused on first-order techniques, such as eliminating numerically insignificant neurons/filters through numerical contribution metric prioritizations, yielding passable improvements

AUGER

GEN

IRS

混亂生活

Tojo Thatchenkery,Keimei Sugiyamatter suited to which topic. We use two models (AsIC, AsLT), each of which is an extension of the well known Independent Cascade (IC) and Linear Threshold (LT) models and incorporates asynchronous time delay. The model parameters are learned by maximizing the likelihood of observation, and the model

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假設(shè)

Tojo Thatchenkery,Keimei Sugiyamarovides a well-calibrated confidence (probability) to indicate the likelihood of the predicted set being correct; for example, an application may automate high-confidence predictions while manually verifying low-confidence predictions. The simplest multi-label classifier, called Binary Relevance (BR