output
20132026
most citedImproving solutions by embedding larger subproblems in a D-Wave quantum annealer

97 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing cs.LGShow all

5 papers · 1 filter

cs.LG2024★ 7 cited

A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks

William Lindskog, Christian Prehofer

Federated Learning (FL) has lately gained traction as it addresses how machine learning models train on distributed datasets. FL was designed for parametric models, namely Deep Neu…

cs.LG2024★ 2 cited

Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data

William Lindskog, Christian Prehofer, Sarandeep Singh

Federated Learning (FL) has gained considerable traction, yet, for tabular data, FL has received less attention. Most FL research has focused on Neural Networks while Tree-Based Mo…

cs.LG2024★ 11 cited

Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

William Lindskog, Christian Prehofer

In this paper, we show how Federated Learning (FL) can be applied to vehicular use-cases in which we seek to classify obstacles, irregularities and pavement types on roads. Our pro…

cs.LG2022★ 10 cited

Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programming

Tadashi Kadowaki, Mitsuru Ambai

In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed,…

cs.LG2019

Breaking Inter-Layer Co-Adaptation by Classifier Anonymization

Ikuro Sato, Kohta Ishikawa, Guoqing Liu +1

This study addresses an issue of co-adaptation between a feature extractor and a classifier in a neural network. A naive joint optimization of a feature extractor and a classifier…