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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Uncovering the mechanisms behind long-term memory is one of the mostfascinating open problems in neuroscience and artificial intelligence.Artificial associative memory networks have been used to formalize importantaspects of biological memory. Generative diffusion models are a type ofgenerative machine learning techniques that have shown great performance inmany tasks. Like associative memory systems, these networks define a dynamicalsystem that converges to a set of target states. In this work we show thatgenerative diffusion models can be interpreted as energy-based models and that,when trained on discrete patterns, their energy function is (asymptotically)identical to that of modern Hopfield networks. This equivalence allows us tointerpret the supervised training of diffusion models as a synaptic learningprocess that encodes the associative dynamics of a modern Hopfield network inthe weight structure of a deep neural network. Leveraging this connection, weformulate a generalized framework for understanding the formation of long-termmemory, where creative generation and memory recall can be seen as parts of aunified continuum.

In search of dispersed memories: Generative diffusion models are associative memory networks