Liouville Flow Importance Sampler
Authors: Yifeng Tian, Nishant Panda, Yen Ting Lin
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of LFIS through its application to a range of benchmark problems, on many of which LFIS achieved state-of-the-art performance. |
| Researcher Affiliation | Academia | 1Information Sciences Group (CCS-3), Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. Correspondence to: Yifeng Tian <yifengtian@lanl.gov>, Yen Ting Lin <yentingl@lanl.gov>. |
| Pseudocode | Yes | Algorithm 1 provides a more detailed description of the implementation of LFIS. |
| Open Source Code | Yes | The code for LFIS and the results of numerical experiments have been deposited at https://github.com/lanl/ LFIS. |
| Open Datasets | Yes | Log Gaussian Cox Process (type-2): ... modeling the positions of Findland pine saplings (Møller et al., 1998). Latent space of Variational Autoencoder (type-2): In this experiment, we investigate sampling in the latent space of a pre-trained Variational Autoencoder (VAE) on the binary MNIST dataset. |
| Dataset Splits | No | The paper discusses training criteria and uses a large number of samples for estimation and smaller batches for gradient descent, but it does not specify explicit validation dataset splits (e.g., percentages or counts for a distinct validation set) or cross-validation setup for hyperparameter tuning. |
| Hardware Specification | Yes | All the experiments are performed using a single NVIDIA A100 GPU with 40GB of RAM. |
| Software Dependencies | No | The paper mentions "Py Torch" and "JAX" but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For all the numerical experiments, we use the Adam optimizer with an initial learning rate of 5 × 10−3. We employ an optimizer schedule that will reduce the learning rate to 50% every 200 epochs without observing any improvement in the loss. At each discrete time step, we used a separate feed-forward NN with a similar structure as in Vargas et al. (2023a) and Zhang & Chen (2022) (two hidden layers, each of which has 64 nodes) to model the discrete-time velocity field. Except for the first (t = 0) NN, which was initialized randomly, we instantiated the NN at t = k/T using the weights of the trained NN at the previous time t = (k − 1)/T to amortize the training cost. We initialized the weights of the last layer of the NN to be zero, which was observed to expedite the training process empirically. |