Customer-obsessed science
Research areas
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December 1, 20258 min read“Network language models” will coordinate complex interactions among intelligent components, computational infrastructure, access points, data centers, and more.
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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
Featured news
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ECIR 20222022Customer reviews are an effective source of information about what people deem important in products (e.g. “strong zipper” for tents). These crowd-created descriptors not only highlight key product attributes, but can also complement seller-provided product descriptions. Motivated by this, we propose to leverage customer reviews to generate queries pertinent to target products in an e-commerce setting.
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AAAI 20222022Robots operating in human spaces must be able to engage in natural language interaction, both understanding and executing instructions, and using conversation to resolve ambiguity and correct mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human–human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates
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QIP 20222022Phase estimation is a quantum algorithm for measuring the eigenvalues of a Hamiltonian. We propose and rigorously analyze a randomized phase estimation algorithm with two distinctive features. First, our algorithm has complexity independent of the number of terms L in the Hamiltonian. Second, unlike previous L-independent approaches, such as those based on qDRIFT, all sources of error in our algorithm can
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IEEE Transactions on Electromagnetic Compatibility2022As more compact designs and more assembled function modules are utilized in modern electronic devices, radiofrequency interference (RFI) source reconstruction is becoming more challenging because different noise sources may contribute simultaneously. This article presents a novel methodology to reconstruct multiple random noise sources on a real-world product, including several double-data-rate (DDR) memory
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WACV 20222022This work presents a No-Reference model to detect audio artifacts in video. The model, based upon a Pretrained Audio Neural Network, classifies a 1-second audio segment as either No Defect, Audio Hum, Audio Hiss, Audio Distortion or Audio Clicks. The model achieves a balanced accuracy of 0.986 on our proprietary simulated dataset.
Collaborations
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