We lifted 7 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| google-deepmind/enn | pwc_unofficial | 7 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| binary_log_likelihood | Ran | google-deepmind/enn/enn/losses/vae_losses.py code served (permissive licence) · get_code("e4d50b188a65c5ad") |
| gaussian_log_likelihood | Ran | google-deepmind/enn/enn/losses/vae_losses.py code served (permissive licence) · get_code("cf2414e3657a07d7") |
| l2_weights_with_predicate | Ran | google-deepmind/enn/enn/losses/utils.py code served (permissive licence) · get_code("1c0c6f90dd25e0cc") |
| latent_kl_divergence | Ran | google-deepmind/enn/enn/losses/vae_losses.py code served (permissive licence) · get_code("d72946fcf12cdc6a") |
| make_default_logger | Ran | google-deepmind/enn/enn/loggers.py code served (permissive licence) · get_code("e30e012ccc8f6abb") |
| serialize | Ran | google-deepmind/enn/enn/loggers.py code served (permissive licence) · get_code("73a9d7f8f0ed39a0") |
| variance_kl | Ran | google-deepmind/enn/enn/losses/prior_losses.py code served (permissive licence) · get_code("be5ccac1b0191bdf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Intelligence relies on an agent's knowledge of what it does not know. This capability can be assessed based on the quality of joint predictions of labels across multiple inputs. In principle, ensemble-based approaches produce effective joint predictions, but the computational costs of training large ensembles can become prohibitive. We introduce the epinet: an architecture that can supplement any conventional neural network, including large pretrained models, and can be trained with modest incremental computation to estimate uncertainty. With an epinet, conventional neural networks outperform very large ensembles, consisting of hundreds or more particles, with orders of magnitude less computation. The epinet does not fit the traditional framework of Bayesian neural networks. To accommodate development of approaches beyond BNNs, such as the epinet, we introduce the epistemic neural network (ENN) as an interface for models that produce joint predictions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2107.08924")
get_code_for_paper("2107.08924")
have("2107.08924")
Connect an agent — have() is free.