Hybrid computing using a neural network with dynamic external memory

By Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwinska, Sergio Gomez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John P. Agapiou and 10 more. 2016

1,765 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Model Architectures

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Citations in the corpus

Structured Attention Networks (2017, methodology): “This approach alleviates the bottleneck of compressing a source into a ﬁxed-dimensional vector by equipping a model with variable-length memory (Weston et al., 2014; Graves et al., 2014; 2016), thereby providing random access into the source as needed.”
Compressive Transformers for Long-Range Sequence Modelling (2019, methodology): “The mostused compression scheme is inspired from the garbage collection mechanism in the Differentiable Neural Computer (Graves et al., 2016) where low-usage memories are erased.”
Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021, neutral): “The related problem of neural algorithm induction has attracted considerable interest (Graves et al., 2014; Kurach et al., 2016; Kaiser & Sutskever, 2016; Graves et al., 2016; Reed & de Freitas, 2016; Veliˇckovi´c et al., 2020a;b).”
Program Synthesis with Large Language Models (2021, neutral): “…are deep network architectures that aim to learn algorithms from input-output examples, by structuring the network in a way that corresponds to mathematical models of computation like Turing machines [Graves et al., 2014, Kaiser and Sutskever, 2016, Kurach et al., 2016, Graves et al., 2016].”
Evaluating Large Language Models Trained on Code (2021, methodology): “…incorporated inductive biases based on modern computing devices, such as the Neural Turing Machine (Graves et al., 2014), memory networks (Weston et al., 2015; Sukhbaatar et al., 2015), the Neural GPU (Kaiser & Sutskever, 2015), and the differentiable neural computer (Graves et al., 2016).”
What can the brain teach us about building artificial intelligence? (2016, neutral): “For example, the Neural Turing Machine (NTM; Graves et al., 2014) and its successor the Differentiable Neural Computer (DNC; Graves et al.”
Programmable Agents (2017, background): “Different approaches to this problem focus on networks that infer programs [3, 10, 22], on networks that execute programs [39, 30, 5] and on networks that jointly do both [14, 17, 38, 29, 13]; although there is much overlap between many of these approaches.”
A simple neural network module for relational reasoning (2017, neutral): “When training jointly on all tasks using 10K examples per task, Memory Networks pass 14/20, DNC 16/20, Sparse DNC 19/20, and EntNet 16/20 (the authors of EntNets report state-of-the-art at 20/20; however, unlike previously reported results this was not done with joint training on all tasks, where th”
Scalable agent alignment via reward modeling: a research direction (2018, background): “…(Anonymous, 2019b), recursion (Cai et al., 2017), graph structure (Battaglia et al., 2018) or natural language (Andreas et al., 2018) in the latent space, differentiable external memory (Graves et al., 2016), or neural units designed to perform arbitrary arithmetic operations (Trask et al., 2018).”
Supervising strong learners by amplifying weak experts (2018, background): “Algorithm learning: our problem differs from traditional work on learning algorithms (Graves et al., 2016; Kaiser and Sutskever, 2015; Neelakantan et al., 2015) because we don’t assume that we have access to ground truth labels.”
Explainable Multi-hop Verbal Reasoning Through Internal Monologue (2021, background): “Typical works include the Differential Neural Computer (DNC) (Graves et al., 2016), End-to-end Memory Networks (Sukhbaatar et al., 2015), Dynamic Memory Networks (DMN) (Kumar et al., 2016) and Compositional Attention Networks (MAC) (Hudson and Manning, 2018).”
Neural Execution Engines: Learning to Execute Subroutines (2020, background): “Recently, researchers have turned their attention towards imbuing neural networks with the capability to perform algorithmic reasoning , thereby allowing them to go beyond pattern recognition and logically solve more complex problems [9, 12, 10, 14].”
Neural Logic Machines (2019, methodology): “MGUncle is deﬁned as one’s maternal great uncle, which is also used by Differentiable Neural Computer (DNC) (Graves et al., 2016).”
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models (2022, background): “Notable examples of such tasks are: (1) Symbolic manipulation (Grefenstette et al., 2015; Joulin & Mikolov, 2015; Kaiser & Sutskever, 2016; Kurach et al., 2016; Graves et al., 2016; Dong et al., 2019), which usually involves manipulating lists of ∗Correspondence to: Denny Zhou <dennyzhou@google.”
Analysing Mathematical Reasoning Abilities of Neural Models (2019, methodology): “But more generally, it is no coincidence that experiments seeking to validate new architectures which aim capture algorithmic/systematic reasoning have often been drawn from this domain (Graves et al., 2016; Kaiser & Sutskever, 2015; Joulin & Mikolov, 2015), and thus in providing a large scale train”
Categorical Reparameterization with Gumbel-Softmax (2016, methodology): “For example, discrete variables have been used to learn probabilistic latent representations that correspond to distinct semantic classes (Kingma et al., 2014), image regions (Xu et al., 2015), and memory locations (Graves et al., 2014; Graves et al., 2016).”