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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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SIGDIAL 20232023The bulk of work adapting transformer models to open-domain dialogue represents dialogue context as the concatenated set of turns in natural language. However, it is unclear if this is the best approach. In this work, we investigate this question by means of an empirical controlled experiment varying the dialogue context format from text-only formats (all recent utterances, summaries, selected utterances
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CIKM 20232023The semantic matching problem in product search seeks to retrieve all semantically relevant products given a user query. Recent studies have shown that extreme multi-label classification (XMC) model enjoys both low inference latency and high recall in real-world scenarios. These XMC semantic matching models adopt TF-IDF vectorizers to extract query text features and use mainly sparse matrices for the model
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2023 IEEE International Conference on Cloud Networking (CloudNet)2023Nowadays, VPN technology is widely used in cloud and hybrid network communication that makes use of algorithms and tunneling to meet different security requirements. However, existing cloud VPN gateways often lack advanced monitoring capabilities and struggle to identify and resolve network connectivity and performance issues. Hence, LPMLP adapted Secure cloud VPN Gateway with Network Monitoring and Issue
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ICCV 20232023In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization. We propose a method that first localizes objects in videos via a slot attention approach and then assigns text to the obtained slots. The latter is achieved by an unsupervised way to read localized semantic information
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tinyML Summit and Research Symposium 20232023Despite the proliferation of diverse hardware accelerators (e.g., NPU, TPU, DPU), deploying deep learning models on edge devices with fixed-point hardware is still challenging due to complex model quantization and conversion. Existing model quantization frameworks like Tensorflow QAT [1], TFLite PTQ [2], and Qualcomm AIMET [3] supports only a limited set of quantization schemes (e.g., only asymmetric per-tensor
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