Zero-Inflated Exponential Family Embeddings
Authors: Li-Ping Liu, David M. Blei
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the Zero-Inflated Embeddings. We compare four models, two baselines and two variants of our model, in the following subsections: 1) EFE is the basic exponential family embedding model; 2) EFE-dz assigns weight 0.1 to zero entries in the training data (same as (Rudolph et al., 2016)); 3) ZIE-0 is the zero-inflated embedding model and fits the exposure probabilities with the intercept term only; and 4) ZIE-cov fits exposure probabilities with covariates. |
| Researcher Affiliation | Academia | 1Columbia University, 500 W 120 St., New York, NY 10027 2Tufts University, 161 College Ave., Medford, MA 02155. |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | No | The code is from the repository constructed by Jastrzebski et al. (2017). This refers to a third-party repository used for evaluation, not the authors' own source code for the proposed method. No statement about releasing their own code. |
| Open Datasets | Yes | All models are evluated with four datasets, e Bird-PA, Movie Lens-100K, Market, and Wiki-S, which will be introduced in detail in the following subsections. Their general information is tabulated in Table 1. |
| Dataset Splits | Yes | One tenth of the training set is separated out as the validation set, whose log-likelihood is used to check whether the optimization procedure converges. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | All models are optimized by Ada Grad (Duchi et al., 2011) implemented in Tensor Flow1. No version numbers are provided for TensorFlow or any other software/libraries. |
| Experiment Setup | Yes | All models are optimized by Ada Grad (Duchi et al., 2011) implemented in Tensor Flow1, and the Ada Grad parameter η for step length is set to 0.1. One tenth of the training set is separated out as the validation set, whose log-likelihood is used to check whether the optimization procedure converges. The variance parameters of α, ρ, and w are set to 1 for all experiments. [...] The embedding dimension K iterates over the set {32, 64, 128}. |