Neural Algorithmic Reasoning: Capturing Classical Computation in Deep Neural Networks
OpenAI
Neural algorithmic reasoning aims to imbue deep neural networks with properties of classical algorithms: provable correctness, strong generalization, interpretability, and compositionality. This article explores how graph neural networks (GNNs) can be designed to align with algorithms like Bellman-Ford, enabling learning to execute such algorithms and generalize to larger inputs. Key innovations include encode-process-decode paradigm, max aggregation, and step-wise supervision.
The article discusses neural algorithmic reasoning, focusing on capturing classical computation (e.g., shortest path-finding, sorting) in deep neural networks. It highlights the importance of algorithmic alignment: designing neural architectures that structurally match the algorithm's data flow. Using Bellman-Ford and graph neural networks (GNNs) as an example, the author explains how GNNs can align with dynamic programming. The author's work on Neural Execution of Graph Algorithms (NEGA) identifies three key inductive biases: encode-process-decode with iterative computation, max aggregation for local optimization, and step-wise supervision to teach invariants. These improvements allow GNNs to generalize to 5x larger inputs. The article traces the lineage from neural Turing machines to modern algorithmic alignment, noting ongoing theoretical developments in linear algorithmic alignment, causal reasoning, and category theory.
- Abbreviations
- GNN = Graph Neural Network — Графовая нейронная сеть
- NEGA = Neural Execution of Graph Algorithms — Нейронное выполнение графовых алгоритмов
- NTM = Neural Turing Machine — Нейронная машина Тьюринга
- DNC = Differentiable Neural Computer — Дифференцируемый нейронный компьютер
Source: The Gradient —
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