Learning Optimal Flows for Non-Equilibrium Importance Sampling
Authors: Yu Cao, Eric Vanden-Eijnden
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the computational side, we show how to use deep learning to represent the velocity field by a neural network and train it towards the zero variance optimum. These results are illustrated numerically on benchmark examples (with dimension up to 10), where after training the velocity field, the variance of the NEIS estimator is reduced by up to 6 orders of magnitude than that of a vanilla estimator. We also compare the performances of NEIS with those of Neal s annealed importance sampling (AIS). |
| Researcher Affiliation | Academia | Yu Cao Courant Institute of Mathematical Sciences, New York University yucaoyc@outlook.com Eric Vanden-Eijnden Courant Institute of Mathematical Sciences, New York University eve2@cims.nyu.edu |
| Pseudocode | No | The paper describes algorithms and methods in text and mathematical formulas but does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | The codes are accessible on https://github.com/yucaoyc/NEIS. |
| Open Datasets | No | The paper uses synthetic distributions (Gaussian mixtures, funnel distribution) from which samples are drawn. It does not use pre-defined, publicly available datasets with typical train/validation/test splits, but rather generates data on the fly based on the specified distributions. |
| Dataset Splits | No | The paper uses synthetic distributions for sampling rather than fixed datasets with explicit train/validation/test splits. Therefore, specific split percentages or counts for validation are not applicable or mentioned. |
| Hardware Specification | Yes | All trainings and estimates of Z1 are conducted on a laptop with CPU i7-12700H; we use 15 threads at maximum. |
| Software Dependencies | No | The paper mentions using deep learning techniques and neural networks but does not specify particular software libraries (e.g., PyTorch, TensorFlow) or their version numbers. |
| Experiment Setup | Yes | We use the finite-time objective Mt ,t+(b) in (7) with t [ 1, 0], t+ = t + 1. ... In practice, we use a time-discretized version of (8) with 2Nt discretization points, and use the standard Runge-Kutta scheme of order 4 (RK4) to integrate the ODE (4) over t [ 1, 1] using uniform time step ( t = 1/Nt). ... we always use an ℓ-layer neural network with width m for all inner layers...The activation function is chosen as the softplus function...We minimize Mt ,t+(b) with respect to the parameters in the neural network using stochastic gradient descent (SGD)... |