Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields
Authors: Yue Zhang, Arti Ramesh
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the ability of the models to learn semantically meaningful structures that also achieve better prediction performance when compared with a greedy search algorithm, a path-based algorithm, and manually deļ¬ned clauses on two computational social science applications: i) modeling recovery in alcohol use disorder, and ii) detecting bullying. |
| Researcher Affiliation | Academia | Yue Zhang and Arti Ramesh SUNY Binghamton {yzhan202, artir}@binghamton.edu |
| Pseudocode | Yes | The description of A3SL is given in Algorithm 1 and the details are given in Algorithm 2. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for their methodology. |
| Open Datasets | Yes | To model recovery from AUD, we use the dataset in Zhang et al. [2018]. |
| Dataset Splits | Yes | Table 1 shows the 5-fold cross-validation results on predicting recovery and relapse of AA-attending users. |
| Hardware Specification | No | The paper mentions running 'multiple agents in parallel' and 'distributed implementation using multi-threading' but provides no specific details about the hardware (e.g., GPU/CPU models, memory) used for experiments. |
| Software Dependencies | No | The paper mentions using 'asynchronous advantage actor-critic (A3C)' but does not provide specific version numbers for any software dependencies, libraries, or programming languages used. |
| Experiment Setup | Yes | In our experiments, the value and the policy network both use a 3-layer feed-forward neural network architecture with tanh as the activation function. We set tmax = 6 and Tmax = 100, 000. |