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.
Episodic Multi-Task Learning with Heterogeneous Neural Processes
Authors: Jiayi Shen, Xiantong Zhen, Qi Wang, Marcel Worring
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 21,472 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,267 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 | Experimental results show the superior performance of the proposed HNPs over typical baselines, and ablation studies verify the effectiveness of the designed inference modules. |
| Researcher Affiliation | Collaboration | 1University of Amsterdam, Netherlands, EMAIL 2 Inception Institute of Artificial Intelligence, Abu Dhabi, UAE, EMAIL 3 Kaiyuan Mathematical Sciences Institute, Changsha, China, EMAIL |
| Pseudocode | Yes | Please refer to Appendix E for algorithms. |
| Open Source Code | Yes | Our code 3 is provided to facilitate such extensions. 3 https://github.com/autumn9999/HNPs.git |
| Open Datasets | Yes | We use Office-Home [91] and Domain Net [92] as episodic multi-task classification datasets. |
| Dataset Splits | No | The paper mentions "numbers of meta-training classes and meta-test classes" for the datasets, but does not provide specific numerical data splits (e.g., percentages or sample counts) for training, validation, and testing sets needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or specific computing environments used for running experiments. |
| Software Dependencies | No | The paper does not list specific version numbers for any software dependencies, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | Following [12, 18], function-fitting tasks are generated with Gaussian processes (GPs). Here a zero mean Gaussian process y(0) GP(0, k( , )) is used to produce y1:4 τ for the inputs from all tasks x1:4 τ . A radial basis kernel k(x, x ) = σ2 exp( (x x )2)/2l2), with l = 0.4 and σ = 1.0 is used. |