Search Results for author: Jose M. F. Moura

Found 5 papers, 1 papers with code

Primal-dual methods for large-scale and distributed convex optimization and data analytics

no code implementations18 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

Towards Aggregating Weighted Feature Attributions

no code implementations20 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.


Few-Shot Human Motion Prediction via Meta-Learning

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.

Few-Shot Learning Human motion prediction +1

Adversarial Geometry-Aware Human Motion Prediction

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.

Decoder Human motion prediction +1

Topology Adaptive Graph Convolutional Networks

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.

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