We lifted 3 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| benrhodes26/tre_code | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| get_dimwise_mixing_ordering_event_shape | Ran | benrhodes26/tre_code/build_bridges.py code served (permissive licence) · get_code("59aa3084ed774116") |
| build_placeholders | Not yet run | benrhodes26/tre_code/representation_learning_evaluation.py code served (permissive licence) · get_code("a64420d5d222f119") |
| nll_loss | Not yet run | benrhodes26/tre_code/representation_learning_evaluation.py code served (permissive licence) · get_code("bc6b7cfb69c9976e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and generative modelling, with the number of use-cases continuing to proliferate. However, it suffers from a critical limitation: it fails to accurately estimate ratios p/q for which the two densities differ significantly. Empirically, we find this occurs whenever the KL divergence between p and q exceeds tens of nats. To resolve this limitation, we introduce a new framework, telescoping density-ratio estimation (TRE), that enables the estimation of ratios between highly dissimilar densities in high-dimensional spaces. Our experiments demonstrate that TRE can yield substantial improvements over existing single-ratio methods for mutual information estimation, representation learning and energy-based modelling.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2006.12204")
get_code_for_paper("2006.12204")
have("2006.12204")
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