Ben Poole, Alexander Alemi, George Tucker, Sherjil Ozair, Aäron Van Den Oord
We lifted 21 functions out of this paper's own repositories and ran 17 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 |
|---|---|---|
| RSMI-NE/RSMI-NE | pwc_unofficial | 12 of 15 |
| karlstratos/doe | — | 4 of 5 |
| Linear95/CLUB | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| CLUB | Ran | Linear95/CLUB/mi_estimators.py pointer only (licence: NONE) · get_code("5d0591b944022c57") |
| Interpolated | Ran | karlstratos/doe/util.py code served (permissive licence) · get_code("d12ee8538feed408") |
| LnConstant | Ran | karlstratos/doe/util.py code served (permissive licence) · get_code("0025679c7875a0c7") |
| LnStandardNormal | Ran | karlstratos/doe/util.py code served (permissive licence) · get_code("2e3c4ed92aa3a733") |
| RSMI_estimate | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/cg_optimisers.py code served (permissive licence) · get_code("6e2da11b3259438f") |
| RSMIdat_filename | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/build_dataset.py code served (permissive licence) · get_code("437b404161244763") |
| array2tensor | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/cg_utils.py code served (permissive licence) · get_code("4183e7ff2acbf08b") |
| connect_bbox | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/plotter.py code served (permissive licence) · get_code("aaece4f3948487e3") |
| construct_reference_graph | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/cg_utils.py code served (permissive licence) · get_code("addf3c7443f250de") |
| eids_from_edges | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/analysis_utils.py code served (permissive licence) · get_code("76a7c4299deb0214") |
| filename | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/build_dataset.py code served (permissive licence) · get_code("1bf1efc3800ca8ce") |
| get_ln_score_function | Ran | karlstratos/doe/util.py code served (permissive licence) · get_code("f13e85f7d9e6310c") |
| iter_loadtxt | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/build_dataset.py code served (permissive licence) · get_code("7b89f9242dbba472") |
| loadNSplit_DimerandVBS | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/cg_utils.py code served (permissive licence) · get_code("9ef4718a1e2a6699") |
| non_collapsed_data | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/analysis_utils.py code served (permissive licence) · get_code("958a914e73170a8f") |
| non_collapsed_estimates | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/analysis_utils.py code served (permissive licence) · get_code("5c3a5208a2efd5f1") |
| round_up | Ran | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/plotter.py code served (permissive licence) · get_code("1b4c5ecd9ab8505e") |
| FF | Not yet run | karlstratos/doe/util.py code served (permissive licence) · get_code("b344d686dec45a3b") |
| cg_configs | Not yet run | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/cg_sequels.py code served (permissive licence) · get_code("3a4cb53a40962c2b") |
| correlator | Not yet run | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/cg_sequels.py code served (permissive licence) · get_code("8c47f52617901f50") |
| mark_inset_hack | Not yet run | RSMI-NE/RSMI-NE/rsmine/coarsegrainer/plotter.py code served (permissive licence) · get_code("1396b54f81fa12c7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and scalable objectives, recent work has turned to variational bounds parameterized by neural networks, but the relationships and tradeoffs between these bounds remains unclear. In this work, we unify these recent developments in a single framework. We find that the existing variational lower bounds degrade when the MI is large, exhibiting either high bias or high variance. To address this problem, we introduce a continuum of lower bounds that encompasses previous bounds and flexibly trades off bias and variance. On high-dimensional, controlled problems, we empirically characterize the bias and variance of the bounds and their gradients and demonstrate the effectiveness of our new bounds for estimation and representation learning.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1905.06922")
get_code_for_paper("1905.06922")
have("1905.06922")
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