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Making most of quite little: Improving AI training for edge sensor time series

Making most of quite little: Improving AI training for edge sensor time series
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Jim bartelsLudovico minatiMasamoto fukawaHiroyuki itoKorkut kaan tokgozChao liNeural network technologyShinshu universityInstitute of innovative researchTokyo institute of technology techNatural sciencesTokyo institute of technologySabanci universityTokyo instituteTokyo techNano sensing unit

School of Computing, Denso IT Laboratory, Inc host event at image recognition conference

School of Computing, Denso IT Laboratory, Inc host event at image recognition conference
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Contrastive Representation Learning

The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning. Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and one negative sample are involved.

Prannay khoslaWang isolaSalakhutdinov hintonJason weiEkind cubukLajanugen logeswaranPhillip isolaDmitry kalenichenkoTianyu gaoYazhe li oriol vinyalsJoshua robinsonLogeswaran leeChing yao chuangMonte carloGeoffrey hintonMathilde caron

Learning with not Enough Data Part 1: Semi-Supervised Learning

The performance of supervised learning tasks improves with more high-quality labels available. However, it is expensive to collect a large number of labeled .

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Contrastive Representation Learning

Contrastive Representation Learning
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Prannay khoslaWang isolaSalakhutdinov hintonJason weiEkind cubukLajanugen logeswaranPhillip isolaDmitry kalenichenkoTianyu gaoYazhe li oriol vinyalsJoshua robinsonLogeswaran leeChing yao chuangMonte carloGeoffrey hintonMathilde caron