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Learning, compression, and leakage: Minimising classification error via meta-universal compression principles

Publications associées (78)

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On Linear Learning with Manycore Processors

Martin Jaggi

A new generation of manycore processors is on the rise that offers dozens and more cores on a chip and, in a sense, fuses host processor and accelerator. In this paper we target the efficient training of generalized linear models on these machines. We prop ...

IEEE COMPUTER SOC2019

Airborne Hyperspectral Imaging of Lakes

Kevin Sylvain Barbieux

In a time of rising concern about climate change and pollution, the water quality of large lakes acts as an indicator of the health of the environment. To study the water quality at a large scale - up to several hundreds of kilometres - hyperspectral remot ...

EPFL2018

Lossy compression of quality scores in differential gene expression: A first assessment and impact analysis

Ana Angelica Hernandez Lopez

High-throughput sequencing of RNA molecules has enabled the quantitative analysis of gene expression at the expense of storage space and processing power. To alleviate these prob- lems, lossy compression methods of the quality scores associated to RNA sequ ...

2018

Point cloud subjective evaluation methodology based on reconstructed surfaces

Touradj Ebrahimi, Evangelos Alexiou

Point clouds have been gaining importance as a solution to the problem of efficient representation of 3D geometric and visual information. They are commonly represented by large amounts of data, and compression schemes are important for their manipulation ...

SPIE-INT SOC OPTICAL ENGINEERING2018

Low-dimensional data embedding for scalable astronomical imaging in the SKA telescope era

Vijay Kartik

Astronomy is one of the oldest sciences known to humanity. We have been studying celestial objects for millennia, and continue to peer deeper into space in our thirst for knowledge about our origins and the universe that surrounds us. Radio astronomy -- ob ...

EPFL2018

MRCZ - A file format for cryo-TEM data with fast compression

Henning Paul-Julius Stahlberg

The introduction of fast CMOS detectors is moving the field of transmission electron microscopy into the computer science field of big data. Automated data pipelines control the instrument and initial processing steps which imposes more onerous data transf ...

Elsevier BV2017

Fully Quantized Distributed Gradient Descent

n major distributed optimization system, the main bottleneck is often the communication between the different machines. To reduce the time dedicated to communications, some heuristics have been developed to reduce the precision of the messages sent and hav ...

2017

Differential Gene Expression with Lossy Compression of Quality Scores in RNA-Seq Data

Marco Mattavelli, Claudio Paolo Alberti, Ana Angelica Hernandez Lopez

High-Throughput sequencing of RNA molecules has enabled the quantitative analysis of the expression of genes at the expense of storage space and processing power. To help alleviate these problems, lossy compression methods of the quality scores associated ...

2017

Learning Active Learning from Data

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection proced ...

2017

Dictionary Compression in Point Cloud Data Management

Anastasia Ailamaki, Mirjana Pavlovic

Nowadays, massive amounts of point cloud data can be collected thanks to advances in data acquisition and processing technologies like dense image matching and airborne LiDAR (Light Detection and Ranging) scanning. With the increase in volume and precision ...

2017