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- W3204138855 abstract "Programs, consisting of semantic and structural information, play an important role in the communication between humans and agents. Towards learning general program executors to unify perception, reasoning, and decision making, we formulate program-guided tasks which require learning to execute a given program on the observed task specification. Furthermore, we propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program by leveraging cross-attention and masked self-attention to pass messages between the specification and routines in the program. ProTo executes a program in a learned latent space and enjoys stronger representation ability than previous neural-symbolic approaches. We demonstrate that ProTo significantly outperforms the previous state-of-the-art methods on GQA visual reasoning and 2D Minecraft policy learning datasets. Additionally, ProTo demonstrates better generalization to unseen, complex, and human-written programs." @default.
- W3204138855 created "2021-10-11" @default.
- W3204138855 creator A5030589527 @default.
- W3204138855 creator A5040617244 @default.
- W3204138855 creator A5073302114 @default.
- W3204138855 creator A5089593268 @default.
- W3204138855 date "2021-10-02" @default.
- W3204138855 modified "2023-10-11" @default.
- W3204138855 title "ProTo: Program-Guided Transformer for Program-Guided Tasks" @default.
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- W3204138855 hasPublicationYear "2021" @default.
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