Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs
Authors: Kevin Course, Prasanth Nair
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4 we provide a number of numerical studies including learning latent neural SDEs from video and performance benchmarking on a motion capture dataset. |
| Researcher Affiliation | Academia | Kevin Course University of Toronto EMAIL Prasanth B. Nair University of Toronto EMAIL |
| Pseudocode | No | The paper describes its method using text and mathematical equations but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps formatted like code. |
| Open Source Code | Yes | All code is available at github.com/coursekevin/arlatentsde. |
| Open Datasets | Yes | In this experiment we consider the motion capture dataset from [32]. The dataset consists of 16 training, 3 validation, and 4 independent test sequences of a subject walking. We made use of the preprocessed data from [34]. |
| Dataset Splits | Yes | We use the first 50 seconds for training and reserve the remaining 15 seconds for validation. |
| Hardware Specification | Yes | Experiments were performed on an Ubuntu server with a dual E5-2680 v3 with a total of 24 cores, 128GB of RAM, and an NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions software like 'Py Torch', 'torchdiffeq', 'torchsde', and 'pytorch_lightning', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In terms of hyperparameters we set the schedule on the KL-divergence to increase from 0 to 1 over 1000 iterations. We choose a learning rate of 10-3 with exponential learning rate decay where the learning rate was decayed lr = γlr every iteration with γ = exp(log(0.9)/1000) (i.e. the effective rate of learning rate decay is lr = 0.9lr every 1000 iterations.). We used the nested Monte-Carlo approximation described in Equation (10) with R = 1, S = 10, and M = 256. |