Customer-obsessed science

Graph Neural Networks (GNNs) require that all nodes have initial representations which are usually derived from the node features. When the node features are absent, GNNs can learn node embeddings with an embedding layer or use pretrained network embeddings for the initial node representations. However, these approaches are limited because i) they cannot be easily extended to initialize new nodes that are

System-in-Package (SiP) technology provides a valuable opportunity to make products with smaller form factor, enrich functionality, and better reliability performance for consumer electronics. One key reason for SiP's success is the encapsulated structure using molding compound, which can provide protection to all the components inside and allows reduced component-to-component spacing. However, if the design

Building numbers shown on building outlines of a map are important information for guiding delivery associates to the correct building of a package’s recipient. Intuitively, the more labeled buildings are present in our map, the less likely to misplace an order in addition to other benefits such as delivery efficiency as drivers get better visual cues about building positions. Although there are free and

Recent years have witnessed a surge of successful applications of machine reading comprehension. Of central importance to these tasks is the availability of massive amount of labeled data, which facilitates training of large-scale neural networks. However, in many real-world problems, annotated data are expensive to gather not only because of time cost and budget, but also of certain domain-specific restrictions

Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years. In addition to learning attention in time domain, recent works also explore learning attention in frequency domains (e.g., Fourier domain, wavelet domain), given that seasonal patterns can be better captured in these domains. In this work, we seek to understand the