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2020

2020

Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations… Expand

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Highly Cited

2015

Highly Cited

2015

We provide algorithms for symbolic integration of hyperlogarithms multiplied by rational functions, which also include multiple… Expand

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Highly Cited

2015

Highly Cited

2015

The goal of neural-symbolic computation is to integrate robust connectionist learning and sound symbolic reasoning. With the… Expand

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Highly Cited

2007

Highly Cited

2007

The human brain possesses the remarkable capability of understanding, interpreting, and producing language, structures, and logic… Expand

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2006

2006

Symbolic computational approaches are desirable for many applications and especially for large complex and nonlinear systems… Expand

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2000

2000

Abstract We present a symbolic technique for computing the exact or approximate solutions of linear differential systems with… Expand

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Highly Cited

1996

Highly Cited

1996

Crosspoint switching array, each crosspoint switching circuit including silicon controlled switches for connecting signal lines… Expand

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Highly Cited

1996

Highly Cited

1996

The LambertW function is defined to be the multivalued inverse of the functionw →wew. It has many applications in pure and… Expand

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1971

1971

Three approaches to symbolic integration in the 1960's are described. The first, from Artificial Intelligence, led to Slagle's… Expand

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Highly Cited

1966

Highly Cited

1966

SIN and SOLDIER are heuristic programs written in LISP which solve symbolic integration problems. SIN (Symbolic INtearator… Expand

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