Fisher Efficient Inference of Intractable Models
Authors: Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical studies validate our asymptotic theorems and we give an example where DLE successfully estimates an intractable model constructed using a pre-trained deep neural network. |
| Researcher Affiliation | Academia | Song Liu University of Bristol The Alan Turing Institute, UK song.liu@bristol.ac.uk Takafumi Kanamori Tokyo Institute of Technology, RIKEN, Japan kanamori@c.titech.ac.jp Wittawat Jitkrittum Max Planck Institute for Intelligent Systems, Germany wittawat@tuebingen.mpg.de Yu Chen University of Bristol, UK yc14600@bristol.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | See https://github.com/lamfeeling/ Stein-Density-Ratio-Estimation for code demos on SDRE and model inference. |
| Open Datasets | Yes | Figure 2: MNIST images with the highest (upper red box) and the lowest unnormalized density (lower green box) estimated on each digit by DLE and NCE. |
| Dataset Splits | No | The paper mentions drawing samples (e.g., "nq = 500 samples" or "nq = 100 randomly selected images") but does not specify explicit training, validation, or test splits by percentages, counts, or pre-defined methodologies. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify particular software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We run DLE 6000 times with new batch of Xq each time and obtain an empirical distribution of nqˆθ. ... For DLE, we set f(x) := [x, x2] and for KSD, we use a polynomial kernel with degree 2. ... The dataset Xqi contains nq = 100 randomly selected images from a single digit i and we use DLE and NCE to estimate ˆθi for each digit i. For DLE, we set f(x) = ψ(x). |