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

Real-world processes often contain intermediate state that can be modeled asan extremely sparse tensor. We introduce Sparling, a technique that allows youto learn models with intermediate layers that match this state from onlyend-to-end labeled examples (i.e., no supervision on the intermediate state).Sparling uses a new kind of informational bottleneck that enforces levels ofactivation sparsity unachievable using other techniques. We find that extremesparsity is necessary to achieve good intermediate state modeling. On oursynthetic DigitCircle domain as well as the LaTeX-OCR and Audio-MNIST-Sequencedomains, we are able to precisely localize the intermediate states up tofeature permutation with > 90% accuracy, even though we only train end-to-end.

SPARLING: Learning Latent Representations with Extremely Sparse Activations