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

Multi-task and multi-domain learning methods seek to learn multipletasks/domains, jointly or one after another, using a single unified network.The key challenge and opportunity is to exploit shared information across tasksand domains to improve the efficiency of the unified network. The efficiencycan be in terms of accuracy, storage cost, computation, or sample complexity.In this paper, we propose a factorized tensor network (FTN) that can achieveaccuracy comparable to independent single-task/domain networks with a smallnumber of additional parameters. FTN uses a frozen backbone network from asource model and incrementally adds task/domain-specific low-rank tensorfactors to the shared frozen network. This approach can adapt to a large numberof target domains and tasks without catastrophic forgetting. Furthermore, FTNrequires a significantly smaller number of task-specific parameters compared toexisting methods. We performed experiments on widely used multi-domain andmulti-task datasets. We show the experiments on convolutional-basedarchitecture with different backbones and on transformer-based architecture. Weobserved that FTN achieves similar accuracy as single-task/domain methods whileusing only a fraction of additional parameters per task.

Factorized Tensor Networks for Multi-Task and Multi-Domain Learning