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Publications associées (30)

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Mask to identify polluted periods during the Arctic Ocean 2018 expedition

Julia Schmale, Andrea Baccarini

Aerosol and trace gas measurements are strongly affected by the ship exhaust and other human activities (e.g., helicopter flights). Therefore, it is important to discriminate between clean and polluted periods. This mask provides an indication of clean per ...

2020

TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation

Pascal Fua, Mateusz Kozinski, Leonardo Citraro, Subeesh Vasu

Most state-of-the-art approaches to road extraction from aerial images rely on a CNN trained to label road pixels as foreground and remainder of the image as background. The CNN is usually trained by minimizing pixel-wise losses, which is less than ideal t ...

2020

Generative adversarial training of product of policies for robust and adaptive movement primitives

Sylvain Calinon, Emmanuel Pignat, Hakan Girgin

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinem ...

2020

Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

Volkan Cevher, Alp Yurtsever

We propose a class of novel variance-reduced stochastic conditional gradient methods. By adopting the recent stochastic path-integrated differential estimator technique (SPIDER) of Fang et al. (2018) for the classical Frank-Wolfe (FW) method, we introduce ...

2019

A Conditional Gradient-Based Augmented Lagrangian Framework

Volkan Cevher, Alp Yurtsever

This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing conditional gradient methods either do not apply to our template or are too slow ...

2019

Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network

Jussi Sakari Leinonen

We demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks. We apply the CGAN to generating two-dimensional ...

AMER GEOPHYSICAL UNION2019

Stochastic Frank-Wolfe for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever

A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed fo ...

2019

Fast and Provable ADMM for Learning with Generative Priors

Volkan Cevher, Armin Eftekhari, Fabian Ricardo Latorre Gomez

In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on the special case where such constraint arises from the specification that a ...

2019

Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

Zhenzhu Meng, Yating Hu, Chenfei Shao

Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monit ...

2019

Sparse Inverse Problems Over Measures: Equivalence Of The Conditional Gradient And Exchange Methods

Armin Eftekhari

We study an optimization program over nonnegative Borel measures that encourages sparsity in its solution. Efficient solvers for this program are in increasing demand, as it arises when learning from data generated by a "continuum-of-subspaces" model, a re ...

SIAM PUBLICATIONS2019

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