Xerox Corporation

Naver Corporation

Michael Niemaz

Julien Perez

Yves Hoppenot

Jean-Marc Andreoli

Denys Proux

Lionel Cazenave

A confidence estimation system includes: a neural network including at least one an attention module including N heads configured to: generate attention matrices based on interactions between tokens for words in an input sequence of words, the input sequence of words including a word that is obscured; and determine the word that is obscured in the input sequence; and a confidence module configured to determine a confidence value indicative of a probability of the neural network correctly determining the word that is obscured, the confidence module determining the confidence value of the word that is obscured using a convolutional neural network that projects the attention matrices generated by the attention module over a multi-dimensional space, the attention matrices recording interactions between the tokens in the input sequence of words without information regarding the tokens for the words and the word that is obscured.

Self-attention-based confidence estimation of language models

Methods and systems input an energy consumption profile for each of a plurality of different sleep modes available for a device, and input a probability distribution of interjob times for the device. The methods and systems then compute the optimal time-out period for each sleep mode based on the energy consumption profile of each sleep mode and the probability distribution of interjob times. Further, such methods and systems monitor the usage of the device to determine the current interjob time, and switch between sleep modes to relatively lower power sleep modes as the current interjob time becomes larger.

Multi-mode device power-saving optimization

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Saint Pierre d'Allevard, France

Michael Niemaz