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

Iterative approximation methods using backpropagation enable the optimizationof neural networks, but they remain computationally expensive, especially whenused at scale. This paper presents an efficient alternative for optimizingneural networks that reduces the costs of scaling neural networks and provideshigh-efficiency optimizations for low-resource applications. We will discuss ageneral result about feed-forward neural networks and then extend this solutionto compositional (mult-layer) networks, which are applied to a simplifiedtransformer block containing feed-forward and self-attention layers. Thesemodels are used to train highly-specified and complex multi-layer neuralarchitectures that we refer to as self-attentive feed-forward unit (SAFFU)layers, which we use to develop a transformer that appears to generalize wellover small, cognitively-feasible, volumes of data. Testing demonstratesexplicit solutions outperform models optimized by backpropagation alone.Moreover, further application of backpropagation after explicit solutions leadsto better optima from smaller scales of data, training effective models frommuch less data is enabled by explicit solution warm starts. We then carry outablation experiments training a roadmap of about 250 transformer models over1-million tokens to determine ideal settings. We find that multiple differentarchitectural variants produce highly-performant models, and discover from thisablation that some of the best are not the most parameterized. This appears toindicate well-generalized models could be reached using less data by usingexplicit solutions, and that architectural exploration using explicit solutionspays dividends in guiding the search for efficient variants with fewerparameters, and which could be incorporated into low-resource hardware where AImight be embodied.

Explicit Foundation Model Optimization with Self-Attentive Feed-Forward Neural Units