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Found 12 papers, 3 papers with code
Diffusion Models for Interferometric Satellite Aperture Radar
Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models, achieving high performance in natural image generation.
Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning
Methane is one of the most potent greenhouse gases, and its short atmospheric half-life makes it a prime target to rapidly curb global warming.
Efficient Online-Bandit Strategies for Minimax Learning Problems
Several learning problems involve solving min-max problems, e. g., empirical distributional robust learning or learning with non-standard aggregated losses.
Linear Bandits on Uniformly Convex Sets
Linear bandit algorithms yield $\tilde{\mathcal{O}}(n\sqrt{T})$ pseudo-regret bounds on compact convex action sets $\mathcal{K}\subset\mathbb{R}^n$ and two types of structural assumptions lead to better pseudo-regret bounds.
Generating Structured Adversarial Attacks Using Frank-Wolfe Method
White box adversarial perturbations are generated via iterative optimization algorithms most often by minimizing an adversarial loss on a $\ell_p$ neighborhood of the original image, the so-called distortion set.
Local and Global Uniform Convexity Conditions
We review various characterizations of uniform convexity and smoothness on norm balls in finite-dimensional spaces and connect results stemming from the geometry of Banach spaces with \textit{scaling inequalities} used in analysing the convergence of optimization methods.
Diptychs of human and machine perceptions
We propose visual creations that put differences in algorithms and humans \emph{perceptions} into perspective.
Trace-Norm Adversarial Examples
White box adversarial perturbations are sought via iterative optimization algorithms most often minimizing an adversarial loss on a $l_p$ neighborhood of the original image, the so-called distortion set.
Interactive Neural Style Transfer with Artists
We present interactive painting processes in which a painter and various neural style transfer algorithms interact on a real canvas.
Reconstructing Latent Orderings by Spectral Clustering
We tackle the task of retrieving linear and circular orderings in a unifying framework, and show how a latent ordering on the data translates into a filamentary structure on the Laplacian embedding.
Data Structures and Algorithms Genomics
Frank-Wolfe with Subsampling Oracle
The first algorithm that we propose is a randomized variant of the original FW algorithm and achieves a $\mathcal{O}(1/t)$ sublinear convergence rate as in the deterministic counterpart.
