Customer-obsessed science
Research areas
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November 28, 20254 min readLarge language models are increasing the accuracy, reliability, and consistency of the product catalogue at scale.
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November 20, 20254 min read
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October 20, 20254 min read
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October 14, 20257 min read
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October 2, 20253 min read
Featured news
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AMIA 20222022Medical coding is a complex task, requiring assignment of a subset of over 72,000 ICD codes to a patient’s notes. Modern natural language processing approaches to these tasks have been challenged by the length of the input and size of the output space. We limit our model inputs to a small window around medical entities found in our documents. From those local contexts, we build contextualized representations
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WACV 20222022Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time SelfTraining
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CIKM 20222022E-commerce marketplaces protect shopper experience and trust at scale by deploying deep learning models trained on human annotated moderation data, for the identification and removal of advert imagery that does not comply with moderation policies (a.k.a. defective images). However, human moderation labels can be hard to source for smaller advert programs that target specific device types with separate formats
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ECCV 2022 Workshop on TiE2022The topics of confidence and trust in modern scene-text recognition (STR) models have been rarely investigated in spite of their prevalent use within critical user-facing applications. We analyze confidence estimation for STR models and find that they tend towards overconfidence thus leading to overestimation of trust in the predicted outcome by users. To overcome this phenomenon we propose a word-level
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CIKM 20222022Cold start is a challenge in product search. Profuse literature addresses related problems such as bias and diversity in search, and cold start is a classic topic in recommender systems research. While search cold start might be seen conceptually as a particular case in such areas, we find that available solutions fail to specifically and practically solve the cold-start problem in product search. The problem
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