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..
Progressive Feature Interaction Search for Deep Sparse Network
Authors: Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, Yong Li
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world benchmark datasets show promising results of PROFIT in both accuracy and ef๏ฌciency. |
| Researcher Affiliation | Collaboration | 1Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University 24Paradigm Inc. |
| Pseudocode | Yes | Algorithm 1 Progressive gradient descent. |
| Open Source Code | No | The paper provides links to baseline implementations but does not provide concrete access to its own source code, nor does it explicitly state that its own code will be released. |
| Open Datasets | Yes | To validate the effectiveness of our proposed PROFIT, we conduct experiments on three benchmark datasets (Criteo, Avazu and ML1M) widely used in existing works of deep sparse networks [5, 31, 18] to evaluate the performance, of which the details are provided in the Appendix. |
| Dataset Splits | Yes | where Dtra and Dval denote the training and validation datasets, respectively. All the other hyper-parameters are tuned on the validation set. |
| Hardware Specification | No | The paper mentions 'our normal hardware platform' but does not provide specific details such as GPU models, CPU types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper states 'We implement our methods using Py Torch' but does not specify the version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We apply Adam with a learning rate of 0.001 and a mini-batch size of 4096, a widely-used setting in existing works [5, 31]. We set the embedding sizes to 16 in all the models. We use the same neural network structure ({400, 400, 400}) for all methods that adopt MLP for a fair comparison, following [5, 31]. |