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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The Web Conference 20222022In online retail stores with ever-increasing catalog, product search is the primary means for customers to discover products of their interest. Surfacing irrelevant products can lead to poor customer experience and in extreme situations loss in engagement. With the recent advances in NLP, Deep Learning models are being used to represent queries and products in shared semantic space to enable semantic sourcing
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EURASIP Journal on Audio, Speech, and Music Processing2022Microphone and speaker array designs have increasingly diverged from simple topologies due to diversity of physical host geometries and use cases. Effective beamformer design must now account for variation in the array’s acoustic radiation pattern, spatial distribution of target and noise sources, and intended beampattern directivity. Relevant tasks such as representing complex pressure fields, specifying
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ACL 20222022We present a complete pipeline to extract characters in a novel and link them to their direct-speech utterances. Our model is divided into three independent components: extracting direct-speech, compiling a list of characters, and attributing those characters to their utterances. Although we find that existing systems can perform the first two tasks accurately, attributing characters to direct speech is
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AISTATS 20222022Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely established in the regime of probabilistic forecasting. In this work, we propose a framework
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AISTATS 20222022We propose Multivariate Quantile Function Forecaster (MQF2), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting. Prior approaches are either autoregressive, implicitly capturing the dependency structure across time but exhibiting error accumulation with increasing forecast horizons, or multi-horizon sequence-to-sequence
Collaborations
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