can significantly reduce the computation time in ML. We initiate a systematic study of the tradeoffs of approximate computation in ML — the success of ML in science | Proposal Forge

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Recipient

can significantly reduce the computation time in ML. We initiate a systematic study of the tradeoffs of approximate computation in ML

Department

Unknown

Program

the success of ML in science

Province

and to attach measures of nonconvexity to nonconvex functions. (3) Distributed computation has become the norm in handling big datasets. We propose the Bounded Asynchronous Protocol (BAP) to better balance communication and computation in distributed ML systems

Type

we propose to investigate two additional structures that are common in ML applications: monotonicity and multi-modality (in the tensor format)

Agreement Number

the recent AlphaGo system of Deepmind that beats top human Go players needed 1900 CPUs and 280 GPUs to carry out the computation. How to balance computation with communication in this vast distributed cluster

Purpose

and commerce

can significantly reduce the computation time in ML. We initiate a systematic study of the tradeoffs of approximate computation in ML × Unknown

2 grants totalling $0

the success of ML in science

1 grants totalling $0

Department

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Program

the success of ML in science →

Recipient Profile

can significantly reduce the computation time in ML. We initiate a systematic study of the tradeoffs of approximate computation in ML →