Publications related to Multiresolution Moment Filters: Theory and Applications

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, John Stephan, Sadegh Farhadkhani, Le Nguyen Hoang, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Metric learning for kernel ridge regression: assessment of molecular similarity

Friedrich Eisenbrand, Puck Elisabeth van Gerwen, Raimon Fabregat I De Aguilar-Amat

Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in c ...

IOP Publishing Ltd2022

Characteristics of a bridge condition assessment method based on state representation methodology (SRM) and damage detection sensitivity

Ayaho Miyamoto

The State Representation Methodology (SRM) combined with the Frequency Slice Wavelet Transform (FSWT), which is a new time-frequency analysis tool, is proposed for assessing the condition of bridges on the basis of bridge monitoring data. First, this paper ...

Springer2019

Light Field Synthesis Using Inexpensive Surveillance Camera Systems

We present a light field synthesis technique that achieves accurate reconstruction given a low-cost, wide-baseline camera rig. Our system integrates optical flow with methods for rectification, disparity estimation, and feature extraction, which we then fe ...

2019

The brain strategy for online learning

Ali H. Sayed, Stefan Vlaski, Bicheng Ying

Complexity is a double-edged sword for learning algorithms when the number of available samples for training in relation to the dimension of the feature space is small. This is because simple models do not sufficiently capture the nuances of the data set, ...

IEEE2016

Fast K-Means with Accurate Bounds

François Fleuret, James Peter Newling

We propose a novel accelerated exact

k

-means algorithm, which outperforms the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3

\times

faster. We also propose a general improvement of existing state-of-the-art a ...

Idiap2016

Fast Detection and Refined Scale Estimation Using Complex Isotropic Wavelets

Michaël Unser, Daniel Sage, Zsuzsanna Püspöki, John Paul Ward

The dyadic scaling in the discrete wavelet transform can lead to a loss of precision, in comparison to the computationally unrealistic continuous wavelet transform. To overcome this obstacle, we propose a novel method to locally scale wavelets between dyad ...

IEEE2015

Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling

Divesh Aggarwal

We give a randomized 2^{n+o(n)}-time and space algorithm for solving the Shortest Vector Problem (SVP) on n-dimensional Euclidean lattices. This improves on the previous fastest algorithm: the deterministic O(4^n)-time and O(2^n)-space algorithm of Miccian ...

ACM2015

Unsupervised Texture Segmentation Using Monogenic Curvelets and the Potts Model

Michaël Unser, Martin Kurt Storath

We present a method for the unsupervised segmentation of textured images using Potts functionals, which are a piecewise-constant variant of the Mumford and Shah functionals. We propose a minimization strategy based on the alternating direction method of mu ...

IEEE2014

Tractable Approaches to Learning and Planning in High Dimensions

Leonidas Lefakis

The continuous increase, witnessed in the last decade, of both the amount of available data and the areas of application of machine learning, has lead to a demand for both learning and planning algorithms that are capable of handling large-scale problems. ...

EPFL2014

Multiresolution Moment Filters: Theory and Applications

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Robust Collaborative Learning with Linear Gradient Overhead

Metric learning for kernel ridge regression: assessment of molecular similarity

Characteristics of a bridge condition assessment method based on state representation methodology (SRM) and damage detection sensitivity

Light Field Synthesis Using Inexpensive Surveillance Camera Systems

The brain strategy for online learning

Fast K-Means with Accurate Bounds

Fast Detection and Refined Scale Estimation Using Complex Isotropic Wavelets

Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling

Unsupervised Texture Segmentation Using Monogenic Curvelets and the Potts Model

Tractable Approaches to Learning and Planning in High Dimensions

Robust Collaborative Learning with Linear Gradient Overhead

Characteristics of a bridge condition assessment method based on state representation methodology (SRM) and damage detection sensitivity

Light Field Synthesis Using Inexpensive Surveillance Camera Systems

Fast Detection and Refined Scale Estimation Using Complex Isotropic Wavelets

The brain strategy for online learning

Tractable Approaches to Learning and Planning in High Dimensions

Metric learning for kernel ridge regression: assessment of molecular similarity

Fast K-Means with Accurate Bounds

Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling

Unsupervised Texture Segmentation Using Monogenic Curvelets and the Potts Model