Customer-obsessed science
Research areas
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February 2, 202610 min readEvery NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football into a sport where every movement is measured, modeled, and instantly analyzed.
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January 13, 20267 min read
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January 8, 20264 min read
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December 29, 20256 min read
Featured news
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NeurIPS 20232023Querying incomplete knowledge graphs (KGs) using deep learning approaches can naturally leverage the reasoning and generalization ability to learn to infer better answers. Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs. However, in the real world, we also need to make logical inferences about events, states, and activities (i.e., eventualities or situations
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NeurIPS 2022 Workshop on Interactive Learning for NLP2023While online shopping, customers often see a product that they have a preference for, but do not purchase it due to not liking a few aspects of the product (e.g., sleeve type or stripe colors on a shirt), and thus have to continue their search. Instead, if the customer were to select a preferred product and issue a modification query, and the system could find a similar product with the desired modification
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EMNLP 20232023In the burgeoning field of natural-language processing, Neural Topic Models (NTMs) and Large Language Models (LLMs) have emerged as areas of significant research interest. Despite this, NTMs have predominantly leveraged contextual embeddings from LLMs, neglecting the potential benefits of harnessing the overall structure. Our study addresses this gap by introducing a novel framework named Diffusion-Enhanced
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EMNLP 20232023Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data, and Continual Learning (CL), the learning of a model from a continual stream of data without keeping the entire history. In CL, the main challenge is forgetting what was learned from past data. While replay-based algorithms
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ACM 2023 Symposium on Operating Systems Principles (SOSP)2023Large deep learning models have recently garnered substantial attention from both academia and industry. Nonetheless, frequent failures are observed during large model training due to large-scale resources involved and extended training time. Existing solutions have significant failure recovery costs due to the severe restriction imposed by the bandwidth of remote storage in which they store checkpoints
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