Customer-obsessed science
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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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ICLR 20232023A locality-sensitive hash (or LSH) is a function that can efficiently map dataset points into a latent space while preserving pairwise distances. Such LSH functions have been used in approximate nearest-neighbor search (ANNS) in the following classic way, which we call classic hash clustering (CHC): first, the dataset points are hashed into a low-dimensional binary space using the LSH function; then, the
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The Web Conference 20232023Interacting with voice assistants, such as Amazon Alexa to aid in day-to-day tasks has become a ubiquitous phenomenon in modernday households. These voice assistants often have screens to provide visual content (e.g., images, videos) to their users. There is an increasing trend of users shopping or searching for products using these devices, yet, these voice assistants do not support commands or queries
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SIGMOD/PODS 20232023Serverless cloud-based warehousing systems enable users to create materialized views in order to speed up predictable and repeated query workloads. Incremental view maintenance (IVM) minimizes the time needed to bring a materialized view up-to-date. It allows the refresh of a materialized view solely based on the base table changes since the last refresh. In serverless cloud-based warehouses, IVM uses computations
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ICASSP 20232023Recent advances in cross-lingual commonsense reasoning (CSR) are facilitated by the development of multilingual pre-trained models (mPTMs). While mPTMs show the potential to encode commonsense knowledge for different languages, transferring commonsense knowledge learned in large-scale English corpus to other languages is challenging. To address this problem, we propose an attentionbased Cross-LIngual Commonsense
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CVPR 20232023Understanding the design of a product without human supervision is a crucial task for e-commerce services. Such a capability can help in multiple downstream e-commerce tasks like product recommendations, design trend analysis, image-based search, and visual information retrieval, etc. For this task, getting fine-grain label data is costly and not scalable for the e-commerce product. In this paper, we leverage
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