Metadata-driven Task Relation Discovery for Multi-task Learning
Authors: Zimu Zheng, Yuqi Wang, Quanyu Dai, Huadi Zheng, Dan Wang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on five real-world datasets demonstrate that the proposed method is effective for MTL with TRD, and particularly useful in complicated systems with diverse metadata but insufficient data samples. |
| Researcher Affiliation | Collaboration | Zimu Zheng1 , Yuqi Wang2 , Quanyu Dai3 , Huadi Zheng3 and Dan Wang3 1The Hong Kong Polytechnic University, Huawei Technologies Co.Ltd. 2The Hong Kong Polytechnic University, Fujian Nebula Big Data Application Service Co., Ltd. 3The Hong Kong Polytechnic University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | ASHRAE RP-884 Thermal Comfort Data (ARP-884) is used to predict occupants feeling of comfort, cold or hot. The data we use in this study is from a Public dataset of ASHRAE, which is a global professional association seeking to advance heating, ventilation, air conditioning, and refrigeration systems design and construction. IBM HK Weather Data (IBM-HWD) is used to predict the temperature in different locations of Hong Kong, China. We collected four-year meteorology data from the Public website of Weather Underground of IBM. Hong Kong Traffic Sensing Data (HK-TSD) is used to predict the traffic speed in Hong Kong, China. We collected the four-month data in six-minute intervals from the Public government website of data.gov.hk. |
| Dataset Splits | Yes | We chronologically order each dataset and use the first 1/2 for training and the remaining 1/4, 1/4 for evaluation and testing. |
| Hardware Specification | Yes | All our experiments are conducted in a private cloud with 16 cores of 2.6GHz CPU and 64G memory. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4). |
| Experiment Setup | Yes | For the parameters, we apply grid searching [Gu and Zhou, 2009] to identify the optimal values. For all clustering algorithms, the number of nearest neighbors is set by searching the grid { ns 2 nc , ns nc , min( 2 ns nc , ns)}, where ns and nc are the number of training samples and clusters. The number of clusters is set as the number of classes in each dataset. For SAMTL and our posterior-phase clustering, cosine similarity is used to compute nearest neighbors as [Zhang et al., 2016]. |