Customer-obsessed science
Research areas
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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically.
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July 9, 202610 min read
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Featured news
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AAAI 2023 Workshop on Artificial Intelligence for User-Centric Assistance for at Home Tasks2023For service robots to become general-purpose in everyday household environments, they need not only a large library of primitive skills, but also the ability to quickly learn novel tasks specified by users. Fine-tuning neural networks on a variety of downstream tasks has been successful in many vision and language domains, but research is still limited on transfer learning between diverse long-horizon tasks
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CHIIR 20232023Open-domain question answering (OpenQA) research has grown rapidly in recent years. However, OpenQA usability evaluation in its real world applications is largely left under studied. In this paper, we evaluated the actual user experience of OpenQA model deployed in a large tech company’s production enterprise search portal. From qualitative query log analysis and user interviews, our preliminary findings
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Who moderates on Twitch and what do they do? Quantifying practices in community moderation on TwitchGROUP 20232023Volunteer moderators are an increasingly essential component of effective community management across a range of services, such as Facebook, Reddit, Discord, YouTube, and Twitch. Prior work has investigated how users of these services become moderators, their attitudes towards community moderation, and the work that they perform, largely through interviews with community moderators and managers. In this
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WACV 2023 Workshop on Pretraining Large Vision and Multimodal Models2023Data augmentation is a necessity to enhance data efficiency in deep learning. For vision-language pre-training, data is only augmented either for images or for text in previous works. In this paper, we present MixGen: a joint data augmentation for vision-language representation learning to further improve data efficiency. It generates new imagetext pairs with semantic relationships preserved by interpolating
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WSDM 20232023Semantic matching is an important component of a product search pipeline. Its goal is to capture the semantic intent of the search query as opposed to the syntactic matching performed by a lexical matching system. A semantic matching model captures relationships like synonyms, and also captures common behavioral patterns to retrieve relevant results by generalizing from purchase data. Semantic matching
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