Episodic Multi-Task Learning with Heterogeneous Neural Processes
Authors: Jiayi Shen, Xiantong Zhen, Qi Wang, Marcel Worring
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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, {j.shen, m.worring}@uva.nl 2 Inception Institute of Artificial Intelligence, Abu Dhabi, UAE, zhenxt@gmail.com 3 Kaiyuan Mathematical Sciences Institute, Changsha, China, hhq123go@gmail.com |
| 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. |