Customer-obsessed science
Research areas
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August 21, 20269 min readExtendable framework enables testing agents on the full set of capabilities required to successfully complete a procedure, not isolated proxy tasks.
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July 30, 20268 min read
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July 9, 202610 min read
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Featured news
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24th IEEE Electronics Packaging Technology Conference (EPTC2022)2022System-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
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ACM SIGSPATIAL 2022 1st International Workshop on Spatial Big Data and AI for Industrial Applications2022Building 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
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Transactions on Machine Learning Research2022Recent 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
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NeurIPS 2022 Workshop on All Things Attention: Bridging Different Perspectives on Attention2022Transformer-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
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NeurIPS 2022 Workshop on Machine Learning for Structural Biology2022Representation learning for proteins is an emerging area in geometric deep learning. Recent works have factored in both the relational (atomic bonds) and the geometric aspects (atomic positions) of the task, notably bringing together graph neural networks (GNNs) with neural networks for point clouds. The equivariances and invariances to geometric transformations (group actions such as rotations and translations
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