Fetching the paper…
Reading the bibliography…
We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain.
Learning context-free grammars: Capabilities and limitations of a recurrent neural network with an external stack memory
Sreerupa Das, C Lee Giles, and Guo-Zheng Sun · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Parsing natural scenes and natural language with recursive neural networks
Richard Socher, Cliff C Lin, Chris Manning, and Andrew Y Ng · 2011
Earlier work this paper cites.
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Transition-based dependency parsing with stack long short-term memory
Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A Smith · 2015
Earlier work this paper cites.
Learning to transduce with unbounded memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, and Phil Blunsom · 2015
Earlier work this paper cites.
Memory networks
Antoine Bordes Jason Weston, Sumit Chopra · 2015
Earlier work this paper cites.
Inferring algorithmic patterns with stack-augmented recurrent nets
Armand Joulin and Tomas Mikolov · 2015
Earlier work this paper cites.
Neural programmer-interpreters
Scott Reed and Nando De Freitas · 2015
Earlier work this paper cites.
End-to-end memory networks
Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al · 2015
Earlier work this paper cites.
Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
Cited alongside, same era.
Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher · 2016
Cited alongside, same era.
Learning continuous semantic representations of symbolic expressions
Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, and Charles Sutton · 2017
Cited alongside, same era.
Making neural programming architectures generalize via recursion
Jonathon Cai, Richard Shin, and Dawn Song · 2017
Cited alongside, same era.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Tree memory networks for modelling long-term temporal dependencies
Tharindu Fernando, Simon Denman, Aaron McFadyen, Sridha Sridharan, and Clinton Fookes · 2018
Later among the works it cites.
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni · 2018
Later among the works it cites.
Graph memory networks for molecular activity prediction
Trang Pham, Truyen Tran, and Svetha Venkatesh · 2018
Later among the works it cites.
Learning to discover efficient mathematical identities
Wojciech Zaremba, Karol Kurach, and Rob Fergus · 2018
Later among the works it cites.
Deep learning for symbolic mathematics
Guillaume Lample and François Charton · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep network guided proof search
Sarah Loos, Geoffrey Irving, Christian Szegedy, and Cezary Kaliszyk · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
The neural network pushdown automaton: Model, stack and learning simulations
Guo-Zheng Sun, C Lee Giles, Hsing-Hen Chen, and Yee-Chun Lee · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Combining symbolic expressions and black-box function evaluations in neural programs
Forough Arabshahi, Sameer Singh, and Animashree Anandkumar · 2018
Cited alongside, same era.
Can neural networks understand logical entailment?
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, and Edward Grefenstette · 2018
Cited alongside, same era.
Closest in time.
Mathematical reasoning in latent space
Dennis Lee, Christian Szegedy, Markus N Rabe, Sarah M Loos, and Kshitij Bansal · 2019
Closest in time.
The neural state pushdown automata
Ankur Mali, Alexander Ororbia, and C Lee Giles · 2019
Closest in time.
Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli · 2019
Closest in time.
Novel positional encodings to enable tree-based transformers
Vighnesh Shiv and Chris Quirk · 2019
Closest in time.
Compositionality decomposed: How do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni · 2020
Closest in time.
Graph neural networks meet neural-symbolic computing: A survey and perspective
Luis Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, and Moshe Vardi · 2020
Closest in time.