Query Complexity of Derivative-Free Optimization

This paper provides lower bounds on the convergence rate of Derivative Free Optimization (DFO) with noisy function evaluations, exposing a fundamental and unavoidable gap between the performance of algorithms with access to gradients and those with access to only function evaluations. However, there are situations in which DFO is unavoidable, and for such situations we propose a new DFO algorithm that is proved to be near optimal for the class of strongly convex objective functions. A distinctive feature of the algorithm is that it uses only Boolean-valued function comparisons, rather than function evaluations. This makes the algorithm useful in an even wider range of applications, such as optimization based on paired comparisons from human subjects, for example. We also show that regardless of whether DFO is based on noisy function evaluations or Boolean-valued function comparisons, the convergence rate is the same

Are there common academic library customer values?

Abstract: Purpose – This paper endeavours to provide answers to the following questions: Is there a correlation between what library customers value and the questions asked in benchmarking satisfaction surveys? Is there a core set of academic library customer values? Are there differences between what academic library customers value in Australia when compared to their counterparts in England? Do library customer values change over time?Design/methodology/approach – The results of two similar university libraries' customer value discovery research are compared with each other, and also with the question set in the LibQUAL+™ survey. As the customer value discovery research was undertaken six years apart, the results are compared to see if there has been change over time.Findings – Academic library customers identified a core set of values, and these values mapped reasonably well to the LibQUAL+™ instrument. However, there were unique value factors identified by the various customer segments that did not map. Some questions in LibQUAL+™ were more detailed in their exploration of library staff attributes than customers identified in their value proposition. Customers identify their values +without reference to library jargon.Originality/value – The paper shows that customer value discovery and LibQUAL+™ are both valuable management tools that identify services and resources of importance to library customer

STNet: Selective Tuning of Convolutional Networks for Object Localization

Visual attention modeling has recently gained momentum in developing visual hierarchies provided by Convolutional Neural Networks. Despite recent successes of feedforward processing on the abstraction of concepts form raw images, the inherent nature of feedback processing has remained computationally controversial. Inspired by the computational models of covert visual attention, we propose the Selective Tuning of Convolutional Networks (STNet). It is composed of both streams of Bottom-Up and Top-Down information processing to selectively tune the visual representation of Convolutional networks. We experimentally evaluate the performance of STNet for the weakly-supervised localization task on the ImageNet benchmark dataset. We demonstrate that STNet not only successfully surpasses the state-of-the-art results but also generates attention-driven class hypothesis maps