Papers by Mattia Opper

1 papers
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure (2023.emnlp-main)

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Challenge: Structured Autoencoder framework StrAE enables effective learning of multi-level representations through strict adherence to explicit structure.
Approach: They propose a Structured Autoencoder framework that strictly adheres to explicit structure and uses a contrastive objective over tree-structured representations.
Outcome: The proposed framework outperforms baselines that don’t involve explicit hierarchical compositions and is comparable to models given informative structure.

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