Assumed Density Filtering Methods for Scalable Learning of Bayesian Neural Networks
In this paper, we first rigorously compare the two algorithms and in the process develop several extensions, including a version of EBP for continuous regression problems and a PBP variant for binary classification.
Line Drawing Video Stylization
We present a method to automatically convert videos and CG animations to stylized animated line-drawings.
AutoConnect: Computational Design of 3D-Printable Connectors
We present AutoConnect, an automatic method that creates customized, 3D-printable connectors attaching two physical objects together.
Proximal Operators for Multi-Agent Path Planning
We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms.
Scalable Methods to Integrate Task Knowledge with the Three-Weight Algorithm for Hybrid Cognitive Processing via Optimization
In this paper, we consider optimization as an approach for quickly and flexibly developing hybrid cognitive capabilities that are efficient, scalable, and can exploit task knowledge to improve solution speed and quality.
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The algorithm builds a forest of trees whose nodes store previously seen examples.
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing
In this paper, we consider optimization as an approach for quickly and flexibly developing hybrid cognitive capabilities that are efficient, scalable, and can exploit knowledge to improve solution speed and quality.
An Improved Three-Weight Message-Passing Algorithm
We describe how the powerful “Divide and Concur” algorithm for constraint satisfaction can be derived as a special case of a message-passing version of the Alternating Direction Method of Multipliers (ADMM) algorithm for convex optimization, and introduce an improved message-passing algorithm based on ADMM/DC by introducing three distinct weights for messages, with “certain” and “no opinion” weights, as well as the standard weight used in ADMM/DC.
A Message-Passing Algorithm for Multi-Agent Trajectory Planning
We describe a novel approach for computing collision-free global trajectories for p agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM) algorithm. Compared with existing methods, our approach is naturally parallelizable and allows for incorporating different cost functionals with only minor adjustments.
Boxelization: Folding 3D Objects into Boxes
We present a method for transforming a 3D object into a cube or a box using a continuous folding sequence.
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