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 [1].
Flexible Transfer Learning under Support and Model Shift
Authors: Xuezhi Wang, Jeff Schneider
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our methods on synthetic data and real-world grape image data. The experimental results show that our transfer learning algorithms significantly outperform existing methods with few labeled target data points. |
| Researcher Affiliation | Academia | Xuezhi Wang Computer Science Department Carnegie Mellon University EMAIL Jeff Schneider Robotics Institute Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper describes the steps of the SMS approach in paragraph form, but it does not provide a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper mentions that code for the 'T/C shift' baseline is available at 'http://people.tuebingen.mpg.de/kzhang/Code-Tar S.zip', but it does not provide code for its own proposed methodology. |
| Open Datasets | Yes | We have two datasets with grape images taken from vineyards and the number of grapes on them as labels, one is riesling (128 labeled images), another is traminette (96 labeled images), as shown in Figure 3. [...] [3] Nuske, S., Gupta, K., Narasihman, S., and Singh., S. Modeling and calibration visual yield estimates in vineyards. International Conference on Field and Service Robotics, 2012. |
| Dataset Splits | Yes | The parameters are chosen by cross-validation. |
| Hardware Specification | Yes | In our real-world dataset with 2177 features, it takes about 2.54 minutes on average in a single-threaded MATLAB process on a 3.1 GHz CPU with 8 GB RAM to solve the objective and recover the transformation. |
| Software Dependencies | No | The paper mentions 'single-threaded MATLAB process' but does not specify a version number for MATLAB or any other software dependencies. |
| Experiment Setup | No | The paper states that 'The parameters are chosen by cross-validation.' but does not provide specific hyperparameter values (e.g., learning rate, batch size) or detailed system-level training settings. |