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Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks, however, they have issues like (a) High Memory Usage, due to duplication of the network layers into multiple copies as experts; and (b) Redundancy in Experts, as common learning-based routing policies suffer from representational collapse.
The Hungarian Method for the Assignment Problem
Harold W. Kuhn · 1955
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett · 2005
Earlier work this paper cites.
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Earlier work this paper cites.
Topology and geometry of half-rectified network optimization
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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