Zobrazeno 1 - 6
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pro vyhledávání: '"GUIBAS, JOHN"'
Vision transformers have delivered tremendous success in representation learning. This is primarily due to effective token mixing through self attention. However, this scales quadratically with the number of pixels, which becomes infeasible for high-
Externí odkaz:
http://arxiv.org/abs/2111.13587
Publikováno v:
PVLDB, 14(11): 2341 - 2354, 2021
Researchers and industry analysts are increasingly interested in computing aggregation queries over large, unstructured datasets with selective predicates that are computed using expensive deep neural networks (DNNs). As these DNNs are expensive and
Externí odkaz:
http://arxiv.org/abs/2108.06313
Given a dataset $\mathcal{D}$, we are interested in computing the mean of a subset of $\mathcal{D}$ which matches a predicate. ABae leverages stratified sampling and proxy models to efficiently compute this statistic given a sampling budget $N$. In t
Externí odkaz:
http://arxiv.org/abs/2107.12525
Publikováno v:
SIGMOD 2022
Unstructured data (e.g., video or text) is now commonly queried by using computationally expensive deep neural networks or human labelers to produce structured information, e.g., object types and positions in video. To accelerate queries, many recent
Externí odkaz:
http://arxiv.org/abs/2009.04540
Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns regarding patient privacy. Typical consent forms only allow for patient
Externí odkaz:
http://arxiv.org/abs/1709.01872
Publikováno v:
ACM/IMS Journal of Data Science; Mar2024, Vol. 1 Issue 1, p1-23, 23p
