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pro vyhledávání: '"Hofer, R. Alex"'
Autor:
Fisch, Adam, Maynez, Joshua, Hofer, R. Alex, Dhingra, Bhuwan, Globerson, Amir, Cohen, William W.
Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data. PPI achieves this by combining small amounts of human-labeled data with larger amounts of data labeled by a reasonably accurate --
Externí odkaz:
http://arxiv.org/abs/2406.04291
Autor:
Hofer, R. Alex, Maynez, Joshua, Dhingra, Bhuwan, Fisch, Adam, Globerson, Amir, Cohen, William W.
Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data. Specifically, PPI methods provide tighter confidence intervals by combining small amounts of human-labeled data with larger amount
Externí odkaz:
http://arxiv.org/abs/2405.06034
We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. This representation enables neural modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to mod
Externí odkaz:
http://arxiv.org/abs/2002.06115
We present efficient differentiable implementations of second-order multi-hop reasoning using a large symbolic knowledge base (KB). We introduce a new operation which can be used to compositionally construct second-order multi-hop templates in a neur
Externí odkaz:
http://arxiv.org/abs/1905.10417