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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.
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July 9, 202610 min read
Featured news
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Data Mining and Knowledge Discovery2022We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is learning-to-rank, the cornerstone of modern search, recommendation system, and information retrieval applications. Empirical ranking datasets do not always follow standard Gaussian distributions or Missing Completely At Random (MCAR) mechanism, which
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KDD 2022 Workshop on Mining and Learning from Time Series – Deep Forecasting: Models, Interpretability, and Applications2022In many forecasting applications (e.g. retail demand, electricity load, weather, finance, etc.), the forecasts must obey certain properties such as having certain context-dependent and time-varying seasonality patterns and avoiding excessive revision as new information becomes available. Here we propose a new forecasting neural net architecture that addresses some of these issues, MQ-Transformer, by incorporating
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IJCAI-ECAI 20222022Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant. State-of-the-art (SOTA) semantic parsers are seq2seq architectures based on large language models that have been pretrained on vast amounts of text. To better leverage that pretraining, recent work has explored a reformulation of semantic parsing whereby the output sequences are themselves
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Interspeech 20222022Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames. Such features, however, may consist not only of information about the spoken language content, but also may contain information about unnecessary contexts such as background noise and sounds or speaker identity, accent, or protected attributes
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HT 20222022Carousel-based interfaces with multiple topic-focused item lists have emerged as a de-facto standard for presenting recommendation results to end-users in real-life recommender systems. In this paper, we attempt to formalize and explain the “magic” power of carousel-based interfaces from a traditional hypertext prospect of navigability. By applying both, formal analysis and a data-driven evaluation, we
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