Neurosymbolic AI Advances State of the Art on Math Word Problems
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Re: Neurosymbolic AI Advances State of the Art on Math Word Problems
#2This article doesn't tell us anything about the Neurosymbolic AI approach except that it performs well and "essentially combines two existing techniques: neural attention Transformers...and tensor product representation".
Re: Neurosymbolic AI Advances State of the Art on Math Word Problems
#3Is the peer-reviewed paper available anywhere yet? This article doesn't tell us anything about the Neurosymbolic AI approach except that it performs well and "essentially combines two existing techniques: neural attention Transformers...and tensor product representation".
Re: Neurosymbolic AI Advances State of the Art on Math Word Problems
#4https://www.microsoft.com/en-us/research/publication/neuro-s...
>The first module, called the cross correlation I/O network, given a set of input-output examples, produces a continuous representation of the set of I/O examples. The second module, the RecursiveReverse-Recursive Neural Network (R3NN), given the continuous representation of the examples, synthesizes a program by incrementally expanding partial programs
I think stuff like this is very interesting and look forward to a future of old-hats complaining that new programmers let the intellisense write all their code for them.
Re: Neurosymbolic AI Advances State of the Art on Math Word Problems
#5Is the peer-reviewed paper available anywhere yet? This article doesn't tell us anything about the Neurosymbolic AI approach except that it performs well and "essentially combines two existing techniques: neural attention Transformers...and tensor product representation".
Re: Neurosymbolic AI Advances State of the Art on Math Word Problems
#6Re: Neurosymbolic AI Advances State of the Art on Math Word Problems
#7James M