Boyao Li, Alexander Thomson, Houssam Nassif, Matthew Engelhard, David Page
We lifted 7 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| engelhard-lab/dnn_treepgm | canonical | 0 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| cont_bern_log_norm | Not yet run | engelhard-lab/dnn_treepgm/model.py code served (permissive licence) · get_code("d34f7dd289b54567") |
| covertype | Not yet run | engelhard-lab/dnn_treepgm/datagen.py code served (permissive licence) · get_code("9fe2624d6c30505c") |
| get_pdf_dataset | Not yet run | engelhard-lab/dnn_treepgm/synthetic.py code served (permissive licence) · get_code("c9160cab40cffc68") |
| make_digit | Not yet run | engelhard-lab/dnn_treepgm/datagen.py code served (permissive licence) · get_code("cecdb4590b47072c") |
| make_moon | Not yet run | engelhard-lab/dnn_treepgm/datagen.py code served (permissive licence) · get_code("8182061f5e947d2b") |
| normal_logpdf | Not yet run | engelhard-lab/dnn_treepgm/model.py code served (permissive licence) · get_code("8f3c00e6c1d9e951") |
| sampling | Not yet run | engelhard-lab/dnn_treepgm/synthetic.py code served (permissive licence) · get_code("3599f178c703e331") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Deep neural networks (DNNs) lack the precise semantics and definitive probabilistic interpretation of probabilistic graphical models (PGMs). In this paper, we propose an innovative solution by constructing infinite tree-structured PGMs that correspond exactly to neural networks. Our research reveals that DNNs, during forward propagation, indeed perform approximations of PGM inference that are precise in this alternative PGM structure. Not only does our research complement existing studies that describe neural networks as kernel machines or infinite-sized Gaussian processes, it also elucidates a more direct approximation that DNNs make to exact inference in PGMs. Potential benefits include improved pedagogy and interpretation of DNNs, and algorithms that can merge the strengths of PGMs and DNNs.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.17583")
get_code_for_paper("2305.17583")
have("2305.17583")
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