Deep learning in robotics: a review of recent research
HA Pierson, MS Gashler - Advanced Robotics, 2017 - Taylor & Francis
Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …
application of deep artificial neural networks to robotic systems, with at least 30 papers …
Machine learning for administrative health records: A systematic review of techniques and applications
Abstract Machine learning provides many powerful and effective techniques for analysing
heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a …
heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a …
Research on classification method of high-dimensional class-imbalanced datasets based on SVM
C Zhang, Y Zhou, J Guo, G Wang, X Wang - International journal of …, 2019 - Springer
High-dimensional problems result in bad classification results because some combinations
of features have an adverse effect on classification; while class-imbalanced problems make …
of features have an adverse effect on classification; while class-imbalanced problems make …
Modeling time series data with deep Fourier neural networks
MS Gashler, SC Ashmore - Neurocomputing, 2016 - Elsevier
We present a method for training a deep neural network containing sinusoidal activation
functions to fit to time-series data. Weights are initialized using a fast Fourier transform, then …
functions to fit to time-series data. Weights are initialized using a fast Fourier transform, then …
Training deep fourier neural networks to fit time-series data
MS Gashler, SC Ashmore - … , ICIC 2014, Taiyuan, China, August 3-6, 2014 …, 2014 - Springer
We present a method for training a deep neural network containing sinusoidal activation
functions to fit to time-series data. Weights are initialized using a fast Fourier transform, then …
functions to fit to time-series data. Weights are initialized using a fast Fourier transform, then …
[PDF][PDF] Waffles: A Machine Learning Toolkit
M Gashler - The Journal of Machine Learning Research, 2011 - jmlr.org
We present a breadth-oriented collection of cross-platform command-line tools for
researchers in machine learning called Waffles. The Waffles tools are designed to offer a …
researchers in machine learning called Waffles. The Waffles tools are designed to offer a …
Robust manifold learning with CycleCut
M Gashler, T Martinez - Connection Science, 2012 - Taylor & Francis
Many manifold learning algorithms utilise graphs of local neighbourhoods to estimate
manifold topology. When neighbourhood connections short-circuit between geodesically …
manifold topology. When neighbourhood connections short-circuit between geodesically …
Comparison of linear dimensionality reduction methods in image annotation
Dimension reduction methods are often used to analyzing high dimensional data, linear
dimension methods are commonly used due to their simple geometric interpretations and for …
dimension methods are commonly used due to their simple geometric interpretations and for …
Applications of nonlinear optimization
Y Xie - 2014 - openscholarship.wustl.edu
Applications of Nonlinear Optimization Page 1 Washington University in St. Louis
Washington University Open Scholarship Arts & Sciences Electronic Theses and …
Washington University Open Scholarship Arts & Sciences Electronic Theses and …
Obstacle avoidance in mobile robots in RGB-D images using deep neural network and semantic segmentation
AAK Al-Adhami, G Cansever - 2022 International Congress on …, 2022 - ieeexplore.ieee.org
Automating industrial and commercial activities through the use of manipulative robots
capable of movement and carrying a variety of products is gaining popularity. When it comes …
capable of movement and carrying a variety of products is gaining popularity. When it comes …