Densely Connected Convolutional Networks
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to ...
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XGBoost
Tree boosting is a highly effective and widely used machine learning method.\nIn this paper, we describe a scalable end-to-end tree boosting system called\nXGBoost, which is use...
LIBSVM
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their...
An Inventory for Measuring Depression
The difficulties inherent in obtaining consistent and adequate diagnoses for the purposes of research and therapy have been pointed out by a number of authors. Pasamanick<sup>12...
Equation of State Calculations by Fast Computing Machines
A general method, suitable for fast computing machines, for investigating such properties as equations of state for substances consisting of interacting individual molecules is ...
Fully convolutional networks for semantic segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, ex...
Self-efficacy: Toward a unifying theory of behavioral change.
The present article presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states th...
The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection.
A 36-item short-form (SF-36) was constructed to survey health status in the Medical Outcomes Study. The SF-36 was designed for use in clinical practice and research, health poli...