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distributed_ml [2018/04/05 19:01] damaskin |
distributed_ml [2018/04/10 14:36] damaskin |
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=== Asynchronous ML on android devices=== | === Asynchronous ML on android devices=== | ||
- | This project is related to training ML algorithms asynchronously on Android devices. The challenges here are primarily: mobile churn, latency, memory, bandwidth and accuracy. The main goal is building a framework to address these challenges. | + | This project is related to training ML algorithms asynchronously on Android devices. The challenges here are primarily: mobile churn, latency, energy consumption, memory, bandwidth and accuracy. |
Related papers:\\ | Related papers:\\ | ||
[1] __[[http://ttic.uchicago.edu/~kgimpel/papers/gimpel+das+smith.conll10.pdf|Distributed Asynchronous Online Learning for Natural Language Processing]]__ \\ | [1] __[[http://ttic.uchicago.edu/~kgimpel/papers/gimpel+das+smith.conll10.pdf|Distributed Asynchronous Online Learning for Natural Language Processing]]__ \\ | ||
- | [2] __[[http://net.pku.edu.cn/~cuibin/Papers/2017%20sigmod.pdf|Heterogeneity-aware Distributed Parameter Servers]]__ | + | [2] __[[http://net.pku.edu.cn/~cuibin/Papers/2017%20sigmod.pdf|Heterogeneity-aware Distributed Parameter Servers]]__ \\ |
+ | [3] __[[http://proceedings.mlr.press/v70/zhang17e.html|ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning]]__ | ||
=== Multi-output multi-class classification === | === Multi-output multi-class classification === |