8,312 research outputs found
A Kernel Perspective for Regularizing Deep Neural Networks
We propose a new point of view for regularizing deep neural networks by using
the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm
cannot be computed, it admits upper and lower approximations leading to various
practical strategies. Specifically, this perspective (i) provides a common
umbrella for many existing regularization principles, including spectral norm
and gradient penalties, or adversarial training, (ii) leads to new effective
regularization penalties, and (iii) suggests hybrid strategies combining lower
and upper bounds to get better approximations of the RKHS norm. We
experimentally show this approach to be effective when learning on small
datasets, or to obtain adversarially robust models.Comment: ICM
Improving customer churn prediction by data augmentation using pictorial stimulus-choice data
The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
We demonstrate the feasibility of a fully automatic computer-aided diagnosis
(CAD) tool, based on deep learning, that localizes and classifies proximal
femur fractures on X-ray images according to the AO classification. The
proposed framework aims to improve patient treatment planning and provide
support for the training of trauma surgeon residents. A database of 1347
clinical radiographic studies was collected. Radiologists and trauma surgeons
annotated all fractures with bounding boxes, and provided a classification
according to the AO standard. The proposed CAD tool for the classification of
radiographs into types "A", "B" and "not-fractured", reaches a F1-score of 87%
and AUC of 0.95, when classifying fractures versus not-fractured cases it
improves up to 94% and 0.98. Prior localization of the fracture results in an
improvement with respect to full image classification. 100% of the predicted
centers of the region of interest are contained in the manually provided
bounding boxes. The system retrieves on average 9 relevant images (from the
same class) out of 10 cases. Our CAD scheme localizes, detects and further
classifies proximal femur fractures achieving results comparable to
expert-level and state-of-the-art performance. Our auxiliary localization model
was highly accurate predicting the region of interest in the radiograph. We
further investigated several strategies of verification for its adoption into
the daily clinical routine. A sensitivity analysis of the size of the ROI and
image retrieval as a clinical use case were presented.Comment: Accepted at IPCAI 2020 and IJCAR
- …