Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 16,343 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,231 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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]. |