On the Sparsity of the Strong Lottery Ticket Hypothesis
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913568329302016 |
|---|---|
| author | Natale, Emanuele Ferre', Davide Giambartolomei, Giordano Giroire, Frédéric Mallmann-Trenn, Frederik |
| author_facet | Natale, Emanuele Ferre', Davide Giambartolomei, Giordano Giroire, Frédéric Mallmann-Trenn, Frederik |
| contents | Considerable research efforts have recently been made to show that a random neural network $N$ contains subnetworks capable of accurately approximating any given neural network that is sufficiently smaller than $N$, without any training. This line of research, known as the Strong Lottery Ticket Hypothesis (SLTH), was originally motivated by the weaker Lottery Ticket Hypothesis, which states that a sufficiently large random neural network $N$ contains \emph{sparse} subnetworks that can be trained efficiently to achieve performance comparable to that of training the entire network $N$. Despite its original motivation, results on the SLTH have so far not provided any guarantee on the size of subnetworks. Such limitation is due to the nature of the main technical tool leveraged by these results, the Random Subset Sum (RSS) Problem. Informally, the RSS Problem asks how large a random i.i.d. sample $Ω$ should be so that we are able to approximate any number in $[-1,1]$, up to an error of $ ε$, as the sum of a suitable subset of $Ω$. We provide the first proof of the SLTH in classical settings, such as dense and equivariant networks, with guarantees on the sparsity of the subnetworks. Central to our results, is the proof of an essentially tight bound on the Random Fixed-Size Subset Sum Problem (RFSS), a variant of the RSS Problem in which we only ask for subsets of a given size, which is of independent interest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14754 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Sparsity of the Strong Lottery Ticket Hypothesis Natale, Emanuele Ferre', Davide Giambartolomei, Giordano Giroire, Frédéric Mallmann-Trenn, Frederik Machine Learning Artificial Intelligence Considerable research efforts have recently been made to show that a random neural network $N$ contains subnetworks capable of accurately approximating any given neural network that is sufficiently smaller than $N$, without any training. This line of research, known as the Strong Lottery Ticket Hypothesis (SLTH), was originally motivated by the weaker Lottery Ticket Hypothesis, which states that a sufficiently large random neural network $N$ contains \emph{sparse} subnetworks that can be trained efficiently to achieve performance comparable to that of training the entire network $N$. Despite its original motivation, results on the SLTH have so far not provided any guarantee on the size of subnetworks. Such limitation is due to the nature of the main technical tool leveraged by these results, the Random Subset Sum (RSS) Problem. Informally, the RSS Problem asks how large a random i.i.d. sample $Ω$ should be so that we are able to approximate any number in $[-1,1]$, up to an error of $ ε$, as the sum of a suitable subset of $Ω$. We provide the first proof of the SLTH in classical settings, such as dense and equivariant networks, with guarantees on the sparsity of the subnetworks. Central to our results, is the proof of an essentially tight bound on the Random Fixed-Size Subset Sum Problem (RFSS), a variant of the RSS Problem in which we only ask for subsets of a given size, which is of independent interest. |
| title | On the Sparsity of the Strong Lottery Ticket Hypothesis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.14754 |