# Dynamic Adaptive Network Intelligence

Source: [Dynamic Adaptive Network Intelligence](<https://www.sota2.com/research/paper/68fa622b1e209131ce9f7d1b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:13:30.351Z

Authors: Richard Searle, Megan Bingham-Walker

Published: 2015-11-19T21:07:27.000Z

## Abstract

Accurate representational learning of both the explicit and implicit relationships within data is critical to the ability of machines to perform more complex and abstract reasoning tasks. We describe the efficient weakly supervised learning of such inferences by our Dynamic Adaptive Network Intelligence (DANI) model. We report state-of-the-art results for DANI over question answering tasks in the bAbI dataset that have proved difficult for contemporary approaches to learning representation (Weston et al., 2015).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1511.06379>)

[PDF](<https://arxiv.org/pdf/1511.06379.pdf>)