32,352 research outputs found
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
A high degree of topical diversity is often considered to be an important
characteristic of interesting text documents. A recent proposal for measuring
topical diversity identifies three elements for assessing diversity: words,
topics, and documents as collections of words. Topic models play a central role
in this approach. Using standard topic models for measuring diversity of
documents is suboptimal due to generality and impurity. General topics only
include common information from a background corpus and are assigned to most of
the documents in the collection. Impure topics contain words that are not
related to the topic; impurity lowers the interpretability of topic models and
impure topics are likely to get assigned to documents erroneously. We propose a
hierarchical re-estimation approach for topic models to combat generality and
impurity; the proposed approach operates at three levels: words, topics, and
documents. Our re-estimation approach for measuring documents' topical
diversity outperforms the state of the art on PubMed dataset which is commonly
used for diversity experiments.Comment: Proceedings of the 39th European Conference on Information Retrieval
(ECIR2017
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection
There has been a recent emergence of sampling-based techniques for estimating
epistemic uncertainty in deep neural networks. While these methods can be
applied to classification or semantic segmentation tasks by simply averaging
samples, this is not the case for object detection, where detection sample
bounding boxes must be accurately associated and merged. A weak merging
strategy can significantly degrade the performance of the detector and yield an
unreliable uncertainty measure. This paper provides the first in-depth
investigation of the effect of different association and merging strategies. We
compare different combinations of three spatial and two semantic affinity
measures with four clustering methods for MC Dropout with a Single Shot
Multi-Box Detector. Our results show that the correct choice of
affinity-clustering combination can greatly improve the effectiveness of the
classification and spatial uncertainty estimation and the resulting object
detection performance. We base our evaluation on a new mix of datasets that
emulate near open-set conditions (semantically similar unknown classes),
distant open-set conditions (semantically dissimilar unknown classes) and the
common closed-set conditions (only known classes).Comment: to appear in IEEE International Conference on Robotics and Automation
2019 (ICRA 2019
Operational Research in Education
Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions
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