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.
Joint Data-Task Generation for Auxiliary Learning
Authors: Hong Chen, Xin Wang, Yuwei Zhou, Yijian Qin, Chaoyu Guan, Wenwu Zhu
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,864 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,232 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 | Extensive experiments show that our proposed DTG-Aux L framework consistently outperforms existing methods in various auxiliary learning scenarios, particularly when the manually collected auxiliary data and tasks are unhelpful. |
| Researcher Affiliation | Academia | Hong Chen1, Xin Wang1,2 , Yuwei Zhou1, Yijian Qin1, Chaoyu Guan1, Wenwu Zhu1,2 1Department of Computer Science and Technology, Tsinghua University 2Beijing National Research Center for Information Science and Technology, Tsinghua |
| Pseudocode | Yes | We summarize the complete algorithm in Appendix 2 |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We conduct our experiments on two scenarios... (i) CUB [25]: ... (ii) CIFAR100 [26]: ... We choose the widely used Amazon Toys and Movies [27] datasets... (i) CIFAR10-100... (ii) Pet-CUB... on the Pet [28] dataset |
| Dataset Splits | No | There is also a validation dataset Dv which is used to evaluate the model performance on the primary task. The paper mentions the use of a validation set but does not provide specific split percentages or sample counts for reproduction. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, memory) used for running the experiments, only model architectures and datasets. |
| Software Dependencies | No | The paper mentions software components like 'Res Net18' and 'Auto INT' and 'MLP' but does not specify version numbers for any programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | In the generator, the embedding dimension mn is searched from {32, 64}, and the layer number of the MLP is searched from {2, 3, 4}. For the head of each task, we adopt Multi-Layer Perceptron(MLP) whose layer is searched from {1, 2}. N is fixed to 3 in all our experiments. |