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

We propose a new, more actionable view of neural network interpretability anddata analysis by leveraging the remarkable matching effectiveness ofrepresentations derived from deep networks, guided by an approach forclass-conditional feature detection. The decomposition of the filter-ngraminteractions of a convolutional neural network and a linear layer over apre-trained deep network yields a strong binary sequence labeler, withflexibility in producing predictions at -- and defining loss functions for --varying label granularities, from the fully-supervised sequence labelingsetting to the challenging zero-shot sequence labeling setting, in which weseek token-level predictions but only have document-level labels for training.From this sequence-labeling layer we derive dense representations of the inputthat can then be matched to instances from training, or a support set withknown labels. Such introspection with inference-time decision rules provides ameans, in some settings, of making local updates to the model by altering thelabels or instances in the support set without re-training the full model.Finally, we construct a particular K-nearest neighbors (K-NN) model frommatched exemplar representations that approximates the original model'spredictions and is at least as effective a predictor with respect to theground-truth labels. This additionally yields interpretable heuristics at thetoken level for determining when predictions are less likely to be reliable,and for screening input dissimilar to the support set. In effect, we show thatwe can transform the deep network into a simple weighting over exemplars andassociated labels, yielding an introspectable -- and modestly updatable --version of the original model.

Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition