Reparameterization Gradient for Non-differentiable Models
Authors: Wonyeol Lee, Hangyeol Yu, Hongseok Yang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compare our gradient estimator (OURS) to the score estimator (SCORE), an unbiased gradient estimator that is applicable to non-differentiable models, and the reparameterization estimator (REPARAM), a biased gradient estimator that computes only Rep Gradθ (discussed in Section 3). We evaluate our estimator on three models for small sequential data: temperature [33] models..., textmsg [1] is a model..., influenza [32] is a model... Table 1 compares the average variance of each estimator for N = 1... Figure 1 shows the ELBO objective, for different estimators with different N s, as a function of the iteration number. |
| Researcher Affiliation | Academia | Wonyeol Lee Hangyeol Yu Hongseok Yang School of Computing, KAIST Daejeon, South Korea {wonyeol, yhk1344, hongseok.yang}@kaist.ac.kr |
| Pseudocode | No | The paper includes mathematical derivations and theorems but does not contain explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code is available at https://github.com/wonyeol/reparam-nondiff. |
| Open Datasets | Yes | temperature [33] models the random dynamics of a controller..., textmsg [1] is a model for the numbers of per-day SNS messages over the period of 74 days..., influenza [32] is a model for the US influenza mortality data in 1969. |
| Dataset Splits | No | The paper uses benchmark models but does not explicitly provide details about train/validation/test dataset splits, specific percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions that the implementation is 'written in Python and uses autograd [18]' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We optimize the ELBO objective using Adam [11] with two stepsizes: 0.001 and 0.01. We run Adam for 10000 iterations and at each iteration, we compute each estimator using N {1, 8, 16} Monte Carlo samples. |