Open Access
Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems
Geoffrey E. Hinton
- pp 159-216
TLDR
This chapter contains sections titled connectionist Representation and Tensor Product Binding: Definition and Examples, and tensor Product Representation: Properties.Abstract:
This chapter contains sections titled: 1 Introduction, 2 Connectionist Representation and Tensor Product Binding: Definition and Examples, 3 Tensor Product Representation: Properties, 4 Conclusionread more
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References
More filters
Book
Deep Learning: Methods and Applications
TL;DR: This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.
Posted Content
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,H. Francis Song,Andrew J. Ballard,Justin Gilmer,George E. Dahl,Ashish Vaswani,Kelsey R. Allen,Charlie Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matthew Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu +26 more
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
Book
Conceptual Spaces: The Geometry of Thought
TL;DR: Peter Gardenfors's theory of conceptual spaces presents a framework for representing information on the conceptual level and shows how conceptual spaces can serve as an explanatory framework for a number of empirical theories, in particular those concerning concept formation, induction, and semantics.
Proceedings Article
Zero-Shot Text-to-Image Generation
Aditya Ramesh,Mikhail Pavlov,Gabriel Goh,Scott Gray,Chelsea Voss,Alec Radford,Mark Chen,Ilya Sutskever +7 more
TL;DR: This work describes a simple approach based on a transformer that autoregressively models the text and image tokens as a single stream of data that is competitive with previous domain-specific models when evaluated in a zero-shot fashion.
Neural Turing Machines
TL;DR: A combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient descent.