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Hard Negative Sampling Strategies for Contrastive Representation Learning
One of the challenges in contrastive learning is the selection of appropriate \textit{hard negative} examples, in the absence of label information.
Adversarial Contrastive Learning by Permuting Cluster Assignments
Contrastive learning has gained popularity as an effective self-supervised representation learning technique.
SAUCE: Truncated Sparse Document Signature Bit-Vectors for Fast Web-Scale Corpus Expansion
In this paper, we consider the problem of fast approximate corpus expansion given a small seed corpus with a few relevant documents as a query, with the goal of capturing the long tail of a domain-specific set of concept terms.
The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research
Using machine learning based text analysis methods, we provide additional evidence that the divergence between these two groups - large firms and non-elite universities - is driven by access to computing power or compute, which we term as the "compute divide".
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