We lifted 1 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 |
|---|---|---|
| sjblim/rnf-ijcnn-2020 | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_network_params | Ran | sjblim/rnf-ijcnn-2020/script_train_fixed_params.py pointer only (licence: NONE) · get_code("146dc3901f899119") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite the recent popularity of deep generative state space models, few comparisons have been made between network architectures and the inference steps of the Bayesian filtering framework -- with most models simultaneously approximating both state transition and update steps with a single recurrent neural network (RNN). In this paper, we introduce the Recurrent Neural Filter (RNF), a novel recurrent autoencoder architecture that learns distinct representations for each Bayesian filtering step, captured by a series of encoders and decoders. Testing this on three real-world time series datasets, we demonstrate that the decoupled representations learnt not only improve the accuracy of one-step-ahead forecasts while providing realistic uncertainty estimates, but also facilitate multistep prediction through the separation of encoder stages.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1901.08096")
get_code_for_paper("1901.08096")
have("1901.08096")
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