English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this study, we present a meta-learning model to adapt the predictions ofthe network's capacity between viewers who participate in a live videostreaming event. We propose the MELANIE model, where an event is formulated asa Markov Decision Process, performing meta-learning on reinforcement learningtasks. By considering a new event as a task, we design an actor-critic learningscheme to compute the optimal policy on estimating the viewers' high-bandwidthconnections. To ensure fast adaptation to new connections or changes amongviewers during an event, we implement a prioritized replay memory buffer basedon the Kullback-Leibler divergence of the reward/throughput of the viewers'connections. Moreover, we adopt a model-agnostic meta-learning framework togenerate a global model from past events. As viewers scarcely participate inseveral events, the challenge resides on how to account for the low structuralsimilarity of different events. To combat this issue, we design a graphsignature buffer to calculate the structural similarities of several streamingevents and adjust the training of the global model accordingly. We evaluate theproposed model on the link weight prediction task on three real-world datasetsof live video streaming events. Our experiments demonstrate the effectivenessof our proposed model, with an average relative gain of 25% againststate-of-the-art strategies. For reproduction purposes, our evaluation datasetsand implementation are publicly available athttps://github.com/stefanosantaris/melanie

Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events