Customer-obsessed science
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November 20, 20254 min readA new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models.
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September 2, 20253 min read
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Featured news
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NeurIPS 20232023Deep learning methods have achieved state-of-the-art performance in most modeling tasks involving images, text and audio, however, they typically underperform tree-based methods on tabular data. In this paper, we hypothesize that a significant contributor to this performance gap is the interaction between irregular target functions resulting from the heterogeneous nature of tabular feature spaces, and the
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RecSys 2023 Workshop on Causality, Counterfactuals & Sequential Decision-Making (CONSEQUENCES)2023Off-Policy Estimation (OPE) methods allow us to learn and evaluate decision-making policies from logged data. This makes them an attractive choice for the offline evaluation of recommender systems, and several recent works have reported successful adoption of OPE methods to this end. An important assumption that makes this work, is the absence of unobserved confounders: random variables that influence both
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EMC+SIPI 20232023Most consumer electronics nowadays integrate multi-radios and high-speed memory interfaces into a very compact form-factor. High speed digital noise is one of common aggressors for desensitization. In this paper, a comprehensive EM simulation workflow is used to analyze the coupling mechanism from the DDR power plane, and optimize the decoupling capacitor value and location to minimize the desense to the
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ASRU 20232023In audiovisual speech recognition (AV-ASR), for many languages only few audiovisual data is available. Building upon an English model, in this work, we first apply and analyze various adapters for cross-language transfer learning to build a parameter-efficient and easy-to-extend AV-ASR in multiple languages. Fine-tuning only the bottleneck adapter with 4% of encoder’s parameters and the decoder shows comparable
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EMNLP 20232023Accurate spelling correction is a critical step in modern search interfaces, especially in an era of mobile devices and speech-to-text inter-faces. For services that are deployed around the world, this poses a significant challenge for multilingual NLP: spelling errors need to be caught and corrected in all languages, and even in queries that use multiple languages. In this paper, we tackle this challenge
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