Search Results for author: Mario Michael Krell

Found 8 papers, 4 papers with code

Packing: Towards 2x NLP BERT Acceleration

1 code implementation29 Jun 2021 Matej Kosec, Sheng Fu, Mario Michael Krell

The shortest-pack-first histogram-packing (SPFHP) algorithm determines the packing order for the Wikipedia dataset of over 16M sequences in 0. 02 seconds.

Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19

2 code implementations23 Dec 2020 Sourabh Kulkarni, Mario Michael Krell, Seth Nabarro, Csaba Andras Moritz

The statistical inference framework is implemented and compared on Intel Xeon CPU, NVIDIA Tesla V100 GPU and the Graphcore Mk1 IPU, and the results are discussed in the context of their computational architectures.

Epidemiology

Accelerating Simulation-based Inference with Emerging AI Hardware

2 code implementations12 Dec 2020 Sourabh Kulkarni, Alexander Tsyplikhin, Mario Michael Krell, Csaba Andras Moritz

As a proof-of-concept, we demonstrate inference over a probabilistic epidemiology model used to predict the spread of COVID-19.

Epidemiology

A First Step Towards Distribution Invariant Regression Metrics

no code implementations10 Sep 2020 Mario Michael Krell, Bilal Wehbe

We show on synthetic and robotic data in reproducible experiments that classical metrics behave wrongly, whereas our new metrics are less sensitive to changing distributions, especially when correcting by the marginal distribution in $X$.

Learning of Multi-Context Models for Autonomous Underwater Vehicles

no code implementations17 Sep 2018 Bilal Wehbe, Octavio Arriaga, Mario Michael Krell, Frank Kirchner

Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system's dynamics.

General Classification

Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data

no code implementations9 Jan 2018 Mario Michael Krell, Anett Seeland, Su Kyoung Kim

On image data, data augmentation is becoming less relevant due to the large amount of available training data and regularization techniques.

Data Augmentation

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