With the growth of global maritime transportation, energy optimization has become crucial for reducing costs and ensuring operational efficiency. Shaft power is the mechanical power transmitted from the engine to the shaft and directly impacts fuel consumption, making its accurate prediction a paramount step in optimizing vessel performance. Power consumption is highly correlated with ship parameters such as speed and shaft rotation per minute, as well as weather and sea conditions. Frequent access to this operational data can improve prediction accuracy. However, obtaining high-quality sensor data is often infeasible and costly, making alternative sources such as noon reports a viable option. In this paper, we propose a transfer learning-based approach for predicting vessels' shaft power, where a model is initially trained on high-frequency data from a vessel and then fine-tuned with low-frequency daily noon reports from other vessels. We tested our approach on sister vessels (identical dimensions and configurations), a similar vessel (slightly larger with a different engine), and a different vessel (distinct dimensions and configurations). The experiments showed that the mean absolute percentage error decreased by 10.6% for sister vessels, 3.6% for a similar vessel, and 5.3% for a different vessel, compared to the model trained solely on noon report data. Keywords: transfer learning, shaft power prediction, noon reports, sensor data, maritime.

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task.

We thank all the reviewers for the time and expertise invested in these reviews. Q: What is the meaning of every notation? Their corresponding lowercase letter refer to one instance in the set, e.g. Q: What is the relationship to other Transfer Learning/Imitation Learning method? Since there are no major flaws pointed out in the review, could the reviewer please raise the overall score?

This enables learning on new tasks using far less data than is required to learn them in isolation.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Event logs reflect the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms these data into value by creating process-related predictions that provide the insights required for proactive interventions at process runtime. Existing PPM techniques require sufficient amounts of event data or other relevant resources that might not be readily available, which prevents some organizations from utilizing PPM. The transfer learning-based PPM technique presented in this paper allows organizations without suitable event data or other relevant resources to implement PPM for effective decision support. This technique is instantiated in both a real-life intra- and an inter-organizational use case, based on which numerical experiments are performed using event logs for IT service management processes. The results of the experiments suggest that knowledge of one business process can be transferred to a similar business process in the same or a different organization to enable effective PPM in the target context. The proposed technique allows organizations to benefit from transfer learning in intra- and inter-organizational settings by transferring resources such as pre-trained models within and across organizational boundaries.

Transfer learning algorithms are used when one has sufficient training data for one supervised learning task (the source/training domain) but only very limited training data for a second task (the target/test domain) that is similar but not identical to the first. Previous work on transfer learning has focused on relatively restricted settings, where specific parts of the model are considered to be carried over between tasks. Recent work on covariate shift focuses on matching the marginal distributions on observations $X$ across domains. Similarly, work on target/conditional shift focuses on matching marginal distributions on labels $Y$ and adjusting conditional distributions $P(X|Y)$, such that $P(X)$ can be matched across domains. However, covariate shift assumes that the support of test $P(X)$ is contained in the support of training $P(X)$, i.e., the training set is richer than the test set. Target/conditional shift makes a similar assumption for $P(Y)$.