We present DLTK, a toolkit providing baseline implementations for efficient experimentation
with deep learning methods on biomedical images. It builds on top of TensorFlow and its …

Learning-to-Rank deals with maximizing the utility of a list of examples presented to the
user, with items of higher relevance being prioritized. It has several practical applications …

Software maintainability predicts changes or failures that may occur in software after it has
been deployed. Since it deals with the degree to which an application may be understood …

This study proposes a multi-objective optimization (MOO) framework for optimizing concrete
mixture proportions. Advanced methods such as K-fold cross-validation, Bayesian …

The development of personalized recommendation has significantly improved the accuracy
of information matching and the revenue of e-commerce platforms. Recently, it has two …

This paper describes the architecture and training of detectors developed for the ALASKA
steganalysis challenge. For each quality factor in the range 60-98, several multi-class tile …

Abstract We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented
in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations. We …

We develop a deep learning–based convolutional-regression model that estimates the
volumetric soil moisture content in the top∼ 5 cm of soil. Input predictors include Sentinel-1 …

In recent years, machine learning has proven to be an extremely useful tool for extracting
knowledge from data. This can be leveraged in numerous research areas, such as …

Multi-dimensional, large-scale, and sparse data, which can be neatly represented by sparse
tensors, are increasingly used in various applications such as data analysis and machine …