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Found 5 papers, 1 papers with code
Topology Adaptive Graph Convolutional Networks
Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss.
Few-Shot Human Motion Prediction via Meta-Learning
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.
Adversarial Geometry-Aware Human Motion Prediction
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.
Towards Aggregating Weighted Feature Attributions
Current approaches for explaining machine learning models fall into two distinct classes: antecedent event influence and value attribution.
Primal-dual methods for large-scale and distributed convex optimization and data analytics
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
