Customer-obsessed science

Recent methods in self-supervised learning have demonstrated that masking-based pretext tasks extend beyond NLP, serving as useful pretraining objectives in computer vision. However, existing approaches apply random or ad hoc masking strategies that limit the difficulty of the reconstruction task and, consequently, the strength of the learnt representations. We improve upon current state-of-the-art work

Electro-Static Discharge (ESD) damage is a common failure mode in the manufacturing process. This paper introduces a continuous ESD monitoring system for New Product Introduction (NPI) line ESD qualification and early detection of potential static charges buildup on devices to defuse the ESD damage risk.

Recent advancements in Natural Language Processing (NLP) algorithms have resulted in state-of-the-art performance on Named Entity Recognition (NER) tasks. These algorithms typically require high-quality labeled datasets for training models. However, training NLP models effectively can suffer from issues such as scarcity of labeled data, data bias and under-representation, and privacy concerns with using

Gradient boosting takes linear combinations of weak base learners. Therefore, absent privacy constraints (when we can exactly optimize over the base models) it is not effective when run over base learner classes that are closed under linear combinations (e.g. linear models). As a result, gradient boosting is typically implemented with tree base learners (e.g., XGBoost), and this has become the state of

Despite recent progress in Natural Language Understanding (NLU), the creation of multilingual NLU systems remains a challenge. It is common to have NLU systems limited to a subset of languages due to lack of available data. They also often vary widely in performance. We launch a three-phase approach to address the limitations in NLU and help propel NLU technology to new heights. We release a 52 language