Fractal encoding of context‐free grammars in connectionist networks

By Whitney Tabor. 2000

67 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Natural Language Processing, Model Architectures

How researchers see this paper

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

In Computational Theory and Mathematics

1. Armand Joulin
2. Tomas Mikolov
3. Joan Bruna
4. Alex W. Nowak
5. Mikael Bodén
6. Janet Wiles
7. Marco Baroni
8. Brenden M. Lake

In Molecular Biology

1. Joan Bruna
2. Alex W. Nowak
3. Mikael Bodén
4. Janet Wiles
5. Tomas Mikolov
6. Armand Joulin

In Natural Language Processing

1. Armand Joulin
2. Tomas Mikolov
3. Joan Bruna
4. Alex W. Nowak
5. Mikael Bodén
6. Janet Wiles
7. Douwe Kiela
8. Edouard Grave

In Model Architectures

1. Armand Joulin
2. Tomas Mikolov
3. Joan Bruna
4. Alex W. Nowak
5. Mikael Bodén
6. Janet Wiles
7. Quoc V. Le
8. Arthur Szlam

Citations in the corpus

• Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets (2015, methodology): “At the same time, our model seems simpler than many other models used for this type of tasks (Tabor, 2000; Bod´en & Wiles, 2000; Graves et al., 2014).”
• Context-free and context-sensitive dynamics in recurrent neural networks (2000, methodology): “RNNs have been handcrafted, sometimes using external stacks, to handle context-free (Das et al., 1993; Zeng et al., 1994; H olldobler et al., 1997; Tabor, 2000) and context-sensitive languages (Steijvers and Gr unwald, 1996).”
• Divide and Conquer with Neural Networks (2017, background): “In particular, dynamic learning appears in works such as [11] and [17].”