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].
Learning to Teach with Dynamic Loss Functions
Authors: Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, Tie-Yan Liu
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real world tasks including image classification and neural machine translation demonstrate that our method significantly improves the quality of various student models. |
| Researcher Affiliation | Collaboration | 1Sun Yat-sen University, Guangzhou, China 2Microsoft Research, Beijing, China 3University of Science and Technology of China, Hefei, China |
| Pseudocode | Yes | Algorithm 1 Training Teacher Model µθ |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about releasing the source code for their method. |
| Open Datasets | Yes | We choose three widely adopted datasets: the MNIST, CIFAR-10 and CIFAR-100 datasets. |
| Dataset Splits | No | The paper mentions 'dev set' and 'development dataset' but does not explicitly provide specific percentages or counts for training, validation, and test splits required for reproduction. |
| 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 using Adam [26] optimizer but does not specify software dependencies with version numbers (e.g., Python, TensorFlow/PyTorch versions). |
| Experiment Setup | Yes | The teacher models are optimized with Adam [26] and the detailed setting is in Appendix. |