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- W4221158021 abstract "Optimization of directed acyclic graph (DAG) structures has many applications, such as neural architecture search (NAS) and probabilistic graphical model learning. Encoding DAGs into real vectors is a dominant component in most neural-network-based DAG optimization frameworks. Currently, most DAG encoders use an asynchronous message passing scheme which sequentially processes nodes according to the dependency between nodes in a DAG. That is, a node must not be processed until all its predecessors are processed. As a result, they are inherently not parallelizable. In this work, we propose a Parallelizable Attention-based Computation structure Encoder (PACE) that processes nodes simultaneously and encodes DAGs in parallel. We demonstrate the superiority of PACE through encoder-dependent optimization subroutines that search the optimal DAG structure based on the learned DAG embeddings. Experiments show that PACE not only improves the effectiveness over previous sequential DAG encoders with a significantly boosted training and inference speed, but also generates smooth latent (DAG encoding) spaces that are beneficial to downstream optimization subroutines. Our source code is available at url{https://github.com/zehao-dong/PACE}" @default.
- W4221158021 created "2022-04-03" @default.
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- W4221158021 date "2022-03-19" @default.
- W4221158021 modified "2023-10-04" @default.
- W4221158021 title "PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs" @default.
- W4221158021 doi "https://doi.org/10.48550/arxiv.2203.10304" @default.
- W4221158021 hasPublicationYear "2022" @default.
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