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

In the appendix, we have the following results. In Appendix A.1, we summarize the main notations used in this paper. In Appendix A.2 - A.9, we show all the proofs of our theoretical results. In Appendix A.10, we present the overall training procedures (e.g., pseudo code) of our proposed DINO-INIT and DINO-TRAIN algorithms, as well as the limitations of our work. Assume that all the parameters of f() follows standard normal distribution, in the limits as the layer width d!1, the output function of the distribution-informed neural network f(x) in Eq (5) at initialization is iid centered Gaussian process, i.e., f() N 0, K Using the definition of the distribution kernel in Eq. (6), we have K It is shown [4] that the key difference between NNGP kernel and NTK is that NTK is generated by a fully-trained neural network, whereas NNGP kernel is produced by a weakly-trained neural network.

It has been proven useful in several settings.

Work on adversarial examples has shown that neural nets are surprisingly sensitive to adversarially chosen changes of small magnitude.

Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data streams from different sources as a strategy to tackle concept drift in a given target domain. These approaches make the assumption that at least one of the source models represents a concept similar to the target concept, which may not hold in many real-world scenarios. In this paper, we propose a novel approach called Multi-source mApping with tRansfer LearnIng for Non-stationary Environments (MARLINE). MARLINE can benefit from knowledge from multiple data sources in non-stationary environments even when source and target concepts do not match. This is achieved by projecting the target concept to the space of each source concept, enabling multiple source sub-classifiers to contribute towards the prediction of the target concept as part of an ensemble. Experiments on several synthetic and real-world datasets show that MARLINE was more accurate than several state-of-the-art data stream learning approaches.

Object Rearrangement is to move objects from an initial state to a goal state.

In the appendix, we have the following results. In Appendix A.1, we summarize the main notations used in this paper. In Appendix A.2 - A.9, we show all the proofs of our theoretical results. In Appendix A.10, we present the overall training procedures (e.g., pseudo code) of our Eq (5) at initialization is iid centered Gaussian process, i.e., f () N 0, K Using the definition of the distribution kernel in Eq. (6), we have NNGP kernel is a special case of NTK when training only the output layer. The objective function of Eq. (7) can be rewritten as follows.

Powder bed fusion (PBF) is an emerging metal additive manufacturing (AM) technology that enables rapid fabrication of complex geometries. However, defects such as pores and balling may occur and lead to structural unconformities, thus compromising the mechanical performance of the part. This has become a critical challenge for quality assurance as the nature of some defects is stochastic during the process and invisible from the exterior. To address this issue, digital twin (DT) using machine learning (ML)-based modeling can be deployed for AM process monitoring and control. Melt pool is one of the most commonly observed physical phenomena for process monitoring, usually by high-speed cameras. Once labeled and preprocessed, the melt pool images are used to train ML-based models for DT applications such as process anomaly detection and print quality evaluation. Nonetheless, the reusability of DTs is restricted due to the wide variability of AM settings, including AM machines and monitoring instruments. The performance of the ML models trained using the dataset collected from one setting is usually compromised when applied to other settings. This paper proposes a knowledge transfer pipeline between different AM settings to enhance the reusability of AM DTs. The source and target datasets are collected from the National Institute of Standards and Technology and National Cheng Kung University with different cameras, materials, AM machines, and process parameters. The proposed pipeline consists of four steps: data preprocessing, data augmentation, domain alignment, and decision alignment. Compared with the model trained only using the source dataset, this pipeline increased the melt pool anomaly detection accuracy by 31% without any labeled training data from the target dataset.