Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Authors: Ting Chen, Martin Renqiang Min, Yizhou Sun
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments with various applications from natural language processing to graph convolutional networks, the total size of the embedding layer can be reduced up to 98% while achieving similar or better performance. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of California, Los Angeles 2NEC Laboratories America. |
| Pseudocode | Yes | Algorithm 1 An epoch of code learning via Straight-through Estimator with Tempering Softmax. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | For the language modeling task, we test on the widely used English Penn Treebank (Marcus et al., 1993) dataset, which contains 1M words with vocabulary size of 10K. |
| Dataset Splits | Yes | The training/validation/test split is provided by convention according to (Mikolov et al., 2010). |
| 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 mentions general software components and frameworks like LSTM and Fast Text but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | By default, we use K = 32, D = 32 and pre-trained distillation guidance for the proposed method, and linear embedding transformation function with 1 hidden layer of 300 hidden units. |