However the resulting models are specialized to a single very specific task and domain. Adapting the learned classification to new domains is a hard problem due to at least three reasons: (1) the new domains and the tasks might be drastically different; (2) there might be very limited ...
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Multi-domain multi-task learning Rebuffi, S.-A., Bilen, H., & Vedaldi, A.Learning multiple visual domains with residual adapters. NeurIPS, 2017. BDD100K [URL] 10-task Driving Dataset Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T....
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning for cross-domain incremental learning, minority-to-majority class for category incremental learning, and small-to-large sample for task incremental learning. ... H Gao,X Wang,Z Chen,... - 《Arxiv》 被引量...
Learning is performed through the open-source RL library smarties54. The library leverages efficiently the computing resources by separating the task of updating the policy parameters from the task of collecting interaction data. The flow simulations are distributed across workers who collect, for each...
First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning). Second, eliminating adverse interactions amongst tasks, which has been shown to significantly degrade the single-task performance...
In order to overcome such a problem, we utilize a multi-domain and multi-task learning framework where we incorporate the five personality and four leadership traits to improve recognition performance in the extraversion and leadership recognition tasks. The flow of the baseline, multi-task, and ...
The resulting features are weighted and combined with those from the time domain. The decoder processes the information from the encoder and ultimately completes the prediction task. Additionally, MFDnet utilizes the GRU-based Seq2Seq framework, which offers several advantages over other prediction ...
DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network. Mathematics 2022, 10, 721. https://doi.org/10.3390/math10050721 AMA Style Xiao Y, Li C, Liu V. DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network. Mathematics. 2022; ...
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is the artificial agent that learns and acts, it is still up to humans to specify the particular task to be performed. ...