Distributed representation NN
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I pesi di questo modello non sono stati pubblicati, quindi non può essere scaricato o eseguito sul vostro hardware a qualsiasi dimensione. È accessibile solo attraverso il suo fornitore, e nessuna scheda grafica può cambiare questo.
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Specifiche complete
Tutto ciò che riguarda questo modello. La maggior parte delle informazioni descrive come è stato addestrato piuttosto che come viene eseguito — un contesto utile per valutare la quantità di lavoro impiegata e come si confronta con modelli realizzati su scala diversa.
Origine
Chi ha costruito questo modello, dove e quando è stato pubblicato.
- Organizzazione
- Carnegie Mellon University (CMU)
- Tipo di organizzazione
- Academia
- Paese
- United States of America
- Pubblicato
- 15 August 1986
- Autori
- Geoffrey E. Hinton
Cosa fa
Le aree problematiche per cui è stato costruito il modello. Un modello può contenere diversi di ciascuno.
- Dominio
- Other
- Compito
- Representation learning
Dimensione
Quanto è grande il modello e su quanti dati è stato addestrato. I parametri sono il valore che decide se si adatta a una determinata scheda grafica.
- Parametri
- 0.4K
- Dati di addestramento
- 100 tokens
- Epoche
- 1,500
Parameters: 24*6 + 12*6 + 12*12 + 12*6 =432 "Figure 5: The activity levels in a five-layer network after it has learned. The bottom layer has 24 input units on the left for representing person 1 and 12 units on the right for representing the relationship. The white squares inside these two groups show the activity levels of the units. There is one active unit in the first group (representing Colin) and one in the second group (representing has-aunt). Each of the two groups of input units is tota…
"After 1500 sweeps through all 100 training examples the weights were very stable "
Addestramento computazionale
L'aritmetica eseguita per addestrare il modello, misurata in operazioni in virgola mobile. È una misura di quanto costato l'addestramento, non di quanto velocemente il modello finito ti risponde.
- Addestramento computazionale
- 3.9 × 10⁸ FLOP
- Come è stato stabilito
- Operation counting
2*432*3*1500*100=388800000=3.9e8 "After 1500 sweeps through all 100 training examples the weights were very stable "
Come è classificato
Etichette che il dataset sorgente applica quando si monitorano modelli notevoli e quanto è fiducioso nell'entry.
- Frontier model
- Yes
- Perché viene tracciato
- Historical significance,Highly cited
- Registrare fiducia
- Confident
Fonti
Da dove proviene questo record e quando è stato controllato l'ultima volta.
- Riferimento
- Learning distributed representations of concepts.
- Ultimo aggiornamento
- 28 November 2025
Cosa significano i numeri
Where it came from
Distributed representation NN was published by Carnegie Mellon University (CMU), in United States of America, in August 1986. It comes out of academia.
It works in Other, and is recorded as doing representation learning.
I suoi pesi non sono mai stati pubblicati, quindi può essere raggiunto solo attraverso il suo fornitore. Nessuna scheda grafica cambia ciò.
Come è stato addestrato
The training run consumed about 3.9 × 10⁸ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 100 tokens went into training it.
Its inclusion criterion is historical significance,Highly cited.
Risposte
Distributed representation NN — Domande frequenti
Is Distributed representation NN open source?
The licensing for Distributed representation NN was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Distributed representation NN have?
Distributed representation NN has 0.4K parameters. Parameters: 24*6 + 12*6 + 12*12 + 12*6 =432 "Figure 5: The activity levels in a five-layer network after it has learned. The bottom layer has 24 input units on the left for representing person 1 and 12 units on the right for representing the relationship. The white squares inside these two groups show the activity levels of the units. There is one active unit in the first group (representing Colin) and one in the second group (representing has-aunt). Each of the two groups of input units is totally connected to its own group of 6 units in the second layer. These two groups of 6 must learn to encode the input terms as distributed patterns of activity. The second layer is totally connected to the central layer of 12 units, and this layer is connected to the penultimate layer of 6 units.". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Distributed representation NN?
Distributed representation NN was published by Carnegie Mellon University (CMU), based in United States of America, categorised as academia.
When was Distributed representation NN released?
Distributed representation NN was published in August 1986. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Distributed representation NN used for?
Distributed representation NN works in Other, and is recorded as handling representation learning. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train Distributed representation NN?
Around 3.9 × 10⁸ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Distributed representation NN?
None. Distributed representation NN is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
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