Roland Mathis
IBM
18 Papers
49 Citations
Roland Mathis is an academic researcher from IBM. The author has contributed to research in topics: Computer science & Interaction network. The author has an hindex of 8, co-authored 17 publications. Previous affiliations of Roland Mathis include École Polytechnique Fédérale de Lausanne & Swiss Federal Institute of Aquatic Science and Technology.
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Papers
Mixed-precision in-memory computing
Manuel Le Gallo,Manuel Le Gallo,Abu Sebastian,Roland Mathis,Matteo Manica,Matteo Manica,Heiner Giefers,Tomas Tuma,Costas Bekas,Alessandro Curioni,Evangelos Eleftheriou +10 more
- 01 Apr 2018
TL;DR: A hybrid system that combines a von Neumann machine with a computational memory unit can offer both the high precision of digital computing and the energy/areal efficiency of in-memory computing, which is illustrated by accurately solving a system of 5,000 equations using 998,752 phase-change memory devices.
Mixed-Precision In-Memory Computing
Manuel Le Gallo,Manuel Le Gallo,Abu Sebastian,Roland Mathis,Matteo Manica,Matteo Manica,Heiner Giefers,Tomas Tuma,Costas Bekas,Alessandro Curioni,Evangelos Eleftheriou +10 more
TL;DR: In this article, a mixed precision in-memory computing (MIMO) system is proposed, which combines a von Neumann machine with a computational memory unit. But it does not address the limitations arising from device variability and nonideal device characteristics.
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Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer.
TL;DR: It is proposed based on the NetBiTE results, that for drugs that inhibit MRPs, the expression of target genes prior to drug administration is a biomarker for IC50 drug sensitivity following drug administration.
Asymmetric cellular memory in bacteria exposed to antibiotics.
TL;DR: It is found that past exposure to low levels of antibiotics increases tolerance to future exposure for the sessile but not for the motile cell, suggesting an evolutionary response to situations where the two cells emerging from division will experience different future conditions.
PIMKL: Pathway Induced Multiple Kernel Learning
TL;DR: Pathway Induced Multiple Kernel Learning (PIMKL) as discussed by the authors exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a multiple kernel learning (MKL) algorithm.