31 citations · 86 across the 12 of their papers we have counts for
6 papers · 1 filter
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes
Matthew L. Weiss, Nathan C. Frey, Siddharth Samsi +2
Traditional frequency based projection filters, or projection operators (PO), separate signal and noise through a series of transformations which remove frequencies where noise is…
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders
Kelum Gajamannage, Yonggi Park, Randy Paffenroth +1
Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the mu…
Neural Network Ensembles: Theory, Training, and the Importance of Explicit Diversity
Wenjing Li, Randy C. Paffenroth, David Berthiaume
Ensemble learning is a process by which multiple base learners are strategically generated and combined into one composite learner. There are two features that are essential to an…
A Pre-training Oracle for Predicting Distances in Social Networks
Gunjan Mahindre, Randy Paffenroth, Anura Jayasumana +1
In this paper, we propose a novel method to make distance predictions in real-world social networks. As predicting missing distances is a difficult problem, we take a two-stage app…
Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders
Rasika Karkare, Randy Paffenroth, Gunjan Mahindre
In this paper, we demonstrate how deep autoencoders can be generalized to the case of inpainting and denoising, even when no clean training data is available. In particular, we sho…
Machine Learning in LiDAR 3D point clouds
F. Patricia Medina, Randy Paffenroth
LiDAR point clouds contain measurements of complicated natural scenes and can be used to update digital elevation models, glacial monitoring, detecting faults and measuring uplift…