Learning Multiple Tasks with Multilinear Relationship Networks
Authors: Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Philip S. Yu
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that MRN yields state-of-the-art results on three multi-task learning datasets. |
| Researcher Affiliation | Academia | Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu School of Software, Tsinghua University, Beijing 100084, China {mingsheng,jimwang}@tsinghua.edu.cn caozhangjie14@gmail.com psyu@uic.edu |
| Pseudocode | No | The paper describes the algorithm steps in paragraph text within Section 4.2 but does not provide a formally structured pseudocode or algorithm block. |
| Open Source Code | No | Codes and datasets will be released. |
| Open Datasets | Yes | Office-Caltech [12] This dataset is the standard benchmark for multi-task learning and transfer learning. ... Office-Home1 [26] ... Image CLEF-DA2 |
| Dataset Splits | Yes | We conduct model selection for all methods using five-fold cross-validation on the training set. |
| Hardware Specification | No | The paper mentions using Alex Net and VGGnet as base architectures, but does not specify any particular hardware (e.g., GPU models, CPU, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions using Alex Net and VGGnet architectures and De CAF7 features, but does not specify any software versions (e.g., Python, TensorFlow, PyTorch versions or specific library versions). |
| Experiment Setup | Yes | As the classifier layer is trained from scratch, we set its learning rate to be 10 times that of the other layers. We use mini-batch stochastic gradient descent (SGD) with 0.9 momentum and learning rate decaying strategy, and select learning rate between 10 5 and 10 2 by stepsize 10 1 2 . |