Hopfield Networks (2020)
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Johannes Kepler University Linz,Institute of Advanced Research in Artificial Intelligence,University of Oslo
- Organisation type
- Academia,Academia,Academia
- Country
- Austria, Norway
- Published
- 16 July 2020
- Authors
- Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Thomas Adler, Lukas Gruber, Markus Holzleitner, Milena Pavlović, Geir Kjetil Sandve, Victor Greiff, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology, Vision, Language, Medicine
- Task
- Drug discovery, Language modeling, Object recognition, Cancer diagnosis
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Unreleased
copyleft-like license, derivative works must retain this license. code here: https://github.com/ml-jku/hopfield-layers/blob/master/LICENSE
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 634
"Hopfield layers yielded a new state-ofthe-art when compared to different machine learning methods. Finally, Hopfield layers achieved state-of-the-art on two drug design datasets" "Our approach has set a new state-of-the-art and has outperformed other methods <..> on the datasets Tiger, Elephant and UCSB Breast Cancer (see Table 1)."
Sources
Where this record came from and when it was last checked.
- Reference
- Hopfield Networks is All You Need
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Hopfield Networks (2020) was published by Johannes Kepler University Linz,Institute of Advanced Research in Artificial Intelligence,University of Oslo, in the country recorded as Austria, during July 2020. The publishing organisation is categorised as academia,Academia,Academia.
It works in the domain of Biology, Vision, Language, Medicine, and is recorded as performing the task of drug discovery, Language modeling, Object recognition, Cancer diagnosis.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
How it was trained
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Hopfield Networks (2020) — common questions
Hopfield Networks (2020)— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
Hopfield Networks (2020)— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Hopfield Networks (2020)— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
Hopfield Networks (2020)— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Hopfield Networks (2020)— who created it?
It was published by Johannes Kepler University Linz,Institute of Advanced Research in Artificial Intelligence,University of Oslo, based in Austria, an organisation categorised as academia,Academia,Academia.
Hopfield Networks (2020)— when was it released?
It was published in July 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Hopfield Networks (2020)— what is it used for?
It works in the domain of Biology, Vision, Language, Medicine, and is recorded as handling the task of drug discovery, Language modeling, Object recognition, Cancer diagnosis. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.