Papers › Logic and the 2-Simplicial Transformer

Logic and the 2-Simplicial Transformer

1 May 2020ICLR 2020 1archive 2025-07-28

James Clift, Dmitry Doryn, Daniel Murfet, James Wallbridge

We introduce the 2-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors. We show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.

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Tasks

Deep Reinforcement Learning Inductive Bias Logical Reasoning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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