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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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ISCA 2021 Speech Synthesis Workshop (SSW11)2021Emotional voice conversion models adapt the emotion in speech without changing the speaker identity or linguistic content. They are less data hungry than text-to-speech models and allow to generate large amounts of emotional data for downstream tasks. In this work we propose EmoCat, a language-agnostic emotional voice conversion model. It achieves high-quality emotion conversion in German with less than
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Neurocomputing2021The central goal of algorithmic fairness is to develop AI-based systems which do not discriminate subgroups in the population with respect to one or multiple notions of inequity, knowing that data is often humanly biased. Researchers are racing to develop AI-based systems able to reach superior performance in terms of accuracy, increasing the risk of inheriting the human biases hidden in the data. An obvious
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NeurIPS 20212021We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged sequence data. It consists of an LSTR encoder that dynamically leverages coarse-scale historical information from an extended temporal window (e.g., 2048 frames spanning of up to 8 minutes), together with an LSTR decoder that focuses
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EACL 20212021Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information etc. With the increasing need to deploy such systems in new domains, solving the problem of zero/fewshot DST has become necessary. There has been a rising trend for learning to transfer knowledge from resource-rich domains to unknown domains
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NeurIPS 20212021Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique that can provide efficient querying mechanism over large and incomplete databases. Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection. However, their geometry is restrictive and leads to non-smooth
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