Mechanism Learning with Mechanism Induced Data
Authors: Tie-Yan Liu, Wei Chen, Tao Qin
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As shown in this paper, there are many interesting research topics along this direction, many of which are still open problems, waiting for researchers in our community to deeply investigate. We show the big picture of MLMID in Figure 1, and will make detailed discussions in the remaining parts of this paper. |
| Researcher Affiliation | Industry | Tie-Yan Liu Microsoft Research tyliu@microsoft.com Wei Chen Microsoft Research wche@microsoft.com Tao Qin Microsoft Research taoqin@microsoft.com |
| Pseudocode | No | The paper includes a conceptual diagram (Figure 1) but no pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any statements about open-source code availability or links to code repositories. |
| Open Datasets | No | The paper discusses the need for "collecting appropriate training data" for the proposed research direction but does not specify or provide access information for any dataset used in its own analysis. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore it does not provide details on training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, therefore no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, therefore no experimental setup details such as hyperparameters or training configurations are provided. |