no code implementations • 18 Dec 2019 • Dusan Jakovetic, Dragana Bajovic, Joao Xavier, Jose M. F. Moura
The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given "difficult" (constrained) problem via finding solutions of a sequence of "easier"(often unconstrained) sub-problems with respect to the original (primal) variable, wherein constraints satisfaction is controlled via the so-called dual variables.
Optimization and Control Information Theory Information Theory
no code implementations • 20 Jan 2019 • Umang Bhatt, Pradeep Ravikumar, Jose M. F. Moura
Current approaches for explaining machine learning models fall into two distinct classes: antecedent event influence and value attribution.
no code implementations • ECCV 2018 • Liang-Yan Gui, Yu-Xiong Wang, Deva Ramanan, Jose M. F. Moura
This paper addresses the problem of few-shot human motion prediction, in the spirit of the recent progress on few-shot learning and meta-learning.
no code implementations • ECCV 2018 • Liang-Yan Gui, Yu-Xiong Wang, Xiaodan Liang, Jose M. F. Moura
We explore an approach to forecasting human motion in a few milliseconds given an input 3D skeleton sequence based on a recurrent encoder-decoder framework.
2 code implementations • ICLR 2018 • Jian Du, Shanghang Zhang, Guanhang Wu, Jose M. F. Moura, Soummya Kar
Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss.