activity
20192022
most citedTowards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation

7 citations · 29 across the 10 of their papers we have counts for

collaborators

14 papers

quant-ph20226 cited

QPack Scores: Quantitative performance metrics for application-oriented quantum computer benchmarking

Huub Donkers, Koen Mesman, Zaid Al-Ars +1

This paper presents the benchmark score definitions of QPack, an application-oriented cross-platform benchmarking suite for quantum computers and simulators, which makes use of sca…

cs.DC2022

Memory-Disaggregated In-Memory Object Store Framework for Big Data Applications

Robin Abrahamse, Akos Hadnagy, Zaid Al-Ars

The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes can directly access memory on adjacent no…

cs.DC20221 cited

Benchmarking Apache Arrow Flight -- A wire-speed protocol for data transfer, querying and microservices

Tanveer Ahmad, Zaid Al Ars, H. Peter Hofstee

Moving structured data between different big data frameworks and/or data warehouses/storage systems often cause significant overhead. Most of the time more than 80\% of the total t…

cs.CV20225 cited

Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features

C. V. Vletter, H. L. Burger, H. Alers +2

Central palsy is a form of facial paralysis that requires urgent medical attention and has to be differentiated from other, similar conditions such as peripheral palsy. To aid in f…

cs.CV20212 cited

An Attention Module for Convolutional Neural Networks

Zhu Baozhou, Peter Hofstee, Jinho Lee +1

Attention mechanism has been regarded as an advanced technique to capture long-range feature interactions and to boost the representation capability for convolutional neural networ…

cs.CV20213 cited

AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression

Baozhou Zhu, Peter Hofstee, Johan Peltenburg +2

Data-free compression raises a new challenge because the original training dataset for a pre-trained model to be compressed is not available due to privacy or transmission issues.…