Block Belief Propagation for Parameter Learning in Markov Random Fields
Authors: You Lu, Zhiyuan Liu, Bert Huang4448-4455
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that the method converges to the same solution as that obtained by using full inference per iteration, despite these approximations, and we empirically demonstrate its scalability improvements over standard training methods. In this section, we empirically analyze the performance of BBPL. We design two groups of experiments. |
| Researcher Affiliation | Academia | You Lu Department of Computer Science Virginia Tech Blacksburg, VA you.lu@vt.edu Zhiyuan Liu Department of Computer Science University of Colorado Boulder Boulder, CO zhiyuan.liu@colorado.edu Bert Huang Department of Computer Science Virginia Tech Blacksburg, VA bhuang@vt.edu |
| Pseudocode | Yes | Algorithm 1 Parameter learning with full convex BP; Algorithm 2 Parameter estimation with block BP |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There are no repository links or explicit statements about code availability. |
| Open Datasets | Yes | For our real data experiments, we use the scene understanding dataset (Gould, Fulton, and Koller 2009) for semantic image segmentation. Each image is 240 x 320 pixels in size. We randomly choose 50 images as the training set and 20 images as the test set. |
| Dataset Splits | Yes | For our real data experiments, we use the scene understanding dataset (Gould, Fulton, and Koller 2009) for semantic image segmentation. Each image is 240 x 320 pixels in size. We randomly choose 50 images as the training set and 20 images as the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions general setups like 'on a GPU'. |
| Software Dependencies | No | The paper mentions using a 'fully convolutional network (FCN)' and fine-tuning parameters from a 'pretrained VGG 16-layer network', but it does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper describes some aspects of feature extraction and network architecture (e.g., 'unary features from a fully convolutional network (FCN)', 'pairwise features are based on those of Domke (2013)', 'discretize it to 10 bins'). However, it does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings in the main text. |