Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations

By Shuai Tang, Paul Smolensky, Virginia de Sa. 2018

4 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Imanol Schlag
2. J. Urgen Schmidhuber
3. Nebojsa Jojic
4. Paul Smolensky
5. Jianfeng Gao
6. Yoshua Bengio
7. Sjoerd van Steenkiste
8. Truyen Tran

Citations in the corpus

• Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving (2019, background): “…to enable the fully distributed representation of symbolic structure (like a hierarchy of features) which has recently resurfaced as an important direction for neural network research (Lake & Baroni, 2017; Bahdanau et al., 2018; van Steenkiste et al., 2019; Palangi et al., 2017; Tang et al., 2018).”
• RNNs Implicitly Implement Tensor Product Representations (2018, unknown): “While our work is orthogonal to this line of work, we note that TPRs and other filler-role representations can profitably be used as an explicit component of neural models (Koniusz et al., 2017; Palangi et al., 2018; Huang et al., 2018; Tang et al., 2018; Schlag & Schmidhuber, 2018).”