Description
The Simple Neural Attention Meta-Learner, or SNAIL, combines the benefits of temporal convolutions and attention to solve meta-learning tasks. They introduce positional dependence through temporal convolutions to make the model applicable to reinforcement tasks - where the observations, actions, and rewards are intrinsically sequential. They also introduce attention in order to provide pinpoint access over an infinitely large context. SNAIL is constructing by combining the two: we use temporal convolutions to produce the context over which we use a causal attention operation.
Papers Using This Method
Multi-scale modeling of Snail-mediated response to hypoxia in tumor progression2024-04-25Modeling how and why aquatic vegetation removal can free rural households from poverty-disease traps2024-01-30Snail Homing and Mating Search Algorithm: A Novel Bio-Inspired Metaheuristic Algorithm2023-10-06Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality2023-07-11Detection and classification of faults aimed at preventive maintenance of PV systems2023-06-13Detection of Malfunctioning Modules in Photovoltaic Power Plants using Unsupervised Feature Clustering Segmentation Algorithm2022-12-30Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation2022-03-24HELIX: Data-driven characterization of Brazilian land snails2021-09-10Anisotropy links cell shapes to tissue flow during convergent extension2020-05-14Sequential Skip Prediction with Few-shot in Streamed Music Contents2019-01-24A Simple Neural Attentive Meta-Learner2017-07-11