ForoughA / neuralMath

Combining Symbolic and Function Evaluation Expressions In Neural Programs
https://openreview.net/forum?id=Hksj2WWAW
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what is black-box function evaluations? #1

Open jiangnanhugo opened 5 years ago

jiangnanhugo commented 5 years ago

In your paper, you mention "black-box" 13 times and "black-box function evaluations" 8 times without providing description about them. And it is also listed as one contribution of your paper, could you help to elaborate about this?

ForoughA commented 5 years ago

black-box function evaluations refers to input-output examples of mathematical functions when the function is treated as a "black-box". To be more clear the input-output pair ($\pi/2$, 1) is a an evaluation for the sine function. Sine is treated as a black-box because we do not care how we got the pair ($\pi/2$, 1), we just use the pair as a black-box evaluation for the function. We use a few such pairs for all the mathematical functions and combine them with the symbolic representation of the functions (e.g. sin^2(x)+cos^2(x)=1).

jiangnanhugo commented 5 years ago

@ForoughA Really thanks for your detailed explanations. Personally, I really like your solid works on building structural models. By the way, I am still confused about the "function evaluation expressions" part. you claim that this is auto-encoder, but from your figure 1(b), I only see this is more similar to siamse network not autoencoder. Also, you mention that $W^-1_{num}$ is the decoder params, does this matrix used to transform "dense embedding into scalar"?. Why not directly do "vector-vecctor product" to calculate the cos similarity?