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• W3207589413 abstract "Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models. It has been empirically observed that the choice of the noise distribution is crucial for NCE's performance. However, such observations have never been made formal or quantitative. In fact, it is not even clear whether the difficulties arising from a poorly chosen noise distribution are statistical or algorithmic in nature. In this work, we formally pinpoint reasons for NCE's poor performance when an inappropriate noise distribution is used. Namely, we prove these challenges arise due to an ill-behaved (more precisely, flat) loss landscape. To address this, we introduce a variant of NCE called eNCE which uses an exponential loss and for which normalized gradient descent addresses the landscape issues provably when the target and noise distributions are in a given exponential family." @default.
• W3207589413 created "2021-10-25" @default.
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• W3207589413 date "2021-10-21" @default.
• W3207589413 modified "2023-09-26" @default.
• W3207589413 title "Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation" @default.
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• W3207589413 doi "https://doi.org/10.48550/arxiv.2110.11271" @default.
• W3207589413 hasPublicationYear "2021" @default.
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