Fast and Exact Enumeration of Deep Networks Partitions Regions
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| Format: | Preprint |
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2024
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| author | Balestriero, Randall LeCun, Yann |
| author_facet | Balestriero, Randall LeCun, Yann |
| contents | One fruitful formulation of Deep Networks (DNs) enabling their theoretical study and providing practical guidelines to practitioners relies on Piecewise Affine Splines. In that realm, a DN's input-mapping is expressed as per-region affine mapping where those regions are implicitly determined by the model's architecture and form a partition of their input space. That partition -- which is involved in all the results spanned from this line of research -- has so far only been computed on $2/3$-dimensional slices of the DN's input space or estimated by random sampling. In this paper, we provide the first parallel algorithm that does exact enumeration of the DN's partition regions. The proposed algorithm enables one to finally assess the closeness of the commonly employed approximations methods, e.g. based on random sampling of the DN input space. One of our key finding is that if one is only interested in regions with ``large'' volume, then uniform sampling of the space is highly efficient, but that if one is also interested in discovering the ``small'' regions of the partition, then uniform sampling is exponentially costly with the DN's input space dimension. On the other hand, our proposed method has complexity scaling linearly with input dimension and the number of regions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_11188 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast and Exact Enumeration of Deep Networks Partitions Regions Balestriero, Randall LeCun, Yann Machine Learning Artificial Intelligence One fruitful formulation of Deep Networks (DNs) enabling their theoretical study and providing practical guidelines to practitioners relies on Piecewise Affine Splines. In that realm, a DN's input-mapping is expressed as per-region affine mapping where those regions are implicitly determined by the model's architecture and form a partition of their input space. That partition -- which is involved in all the results spanned from this line of research -- has so far only been computed on $2/3$-dimensional slices of the DN's input space or estimated by random sampling. In this paper, we provide the first parallel algorithm that does exact enumeration of the DN's partition regions. The proposed algorithm enables one to finally assess the closeness of the commonly employed approximations methods, e.g. based on random sampling of the DN input space. One of our key finding is that if one is only interested in regions with ``large'' volume, then uniform sampling of the space is highly efficient, but that if one is also interested in discovering the ``small'' regions of the partition, then uniform sampling is exponentially costly with the DN's input space dimension. On the other hand, our proposed method has complexity scaling linearly with input dimension and the number of regions. |
| title | Fast and Exact Enumeration of Deep Networks Partitions Regions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.11188 |