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Hyperbolic Neural Networks++

ICLR 2021 · Architectures and efficiency › CNN design · approach: Theory of convolutional inductive bias · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use hyperbolic RNN+FFNN with Euclidean MLR instead of a fully Euclidean RNNRNN sentence-pair classifier · PREFIX-10%accuracy: 89.62 % -> 96.36 %Helped
Use a fully hyperbolic RNN instead of a fully Euclidean RNNRNN sentence-pair classifier · PREFIX-50%accuracy: 72.10 % -> 62.94 %Hurt
Use hyperbolic GRU+FFNN with Euclidean MLR instead of a fully Euclidean GRUGRU sentence-pair classifier · PREFIX-30%accuracy: 86.47 % -> 88.47 %Helped
Use hyperbolic rather than Euclidean sentence encodersSNLI sentence-pair classifier · SNLIaccuracy: 79.34 % -> 79.18 %; 81.52 % -> 81.19 %No clear effect
Use hyperbolic MLR rather than Euclidean MLR on Poincaré embeddingsWordNet subtree classifier · WordNet subtrees, d=5F1: 98.07 ± 0.55% vs 95.59 ± 1.18%; 87.89 ± 0.80% vs 67.82 ± 0.81%; 55.46 ± 5.49% vs 35.23 ± 3.16%; 88.73 ± 3.22% vs 59.35 ±Helped
Use hyperbolic MLR rather than Euclidean MLR after mapping embeddings to the tangent spaceWordNet subtree classifier · WordNet subtrees, d=5F1: 98.07 ± 0.55% vs 89.21 ± 1.34%; 87.89 ± 0.80% vs 67.92 ± 0.74%; 55.46 ± 5.49% vs 26.47 ± 0.78%; 88.73 ± 3.22% vs 52.51 ±Helped
Use identity rather than tanh in the hyperbolic RNN/GRU cell and identity rather than ReLU after the hyperbolic FFNN (ablation)Hyperbolic sentence classifier · SNLI and PREFIX datasetsaccuracyHelped
Use tanh and ReLU rather than identity nonlinearities in the Euclidean models (ablation)Fully Euclidean sentence classifier · SNLI and PREFIX datasetsaccuracyHelped

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