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Explore context-sensitive interactions in probabilistic grammars, log-linear models, and stochastic unification-based grammars with implications for optimality theory.
Explore acoustic event detection for AI systems, focusing on Alexa Guard's development and research to improve AED model accuracy and efficiency.
Explore listener-oriented phonology, focusing on French vowel-initial words and Optimality Theory. Learn how this approach handles complex phonological processes and improves constraint formulation.
Explore BitFunnel algorithm powering Bing search, using Bloom filters to efficiently process queries and search billions of documents, challenging conventional wisdom in text indexing.
Explore new methods for capturing and exploiting multiscale speech dynamics, including formant estimation and adaptive short-time Fourier analysis for speech enhancement.
Explore OCP-Place constraint in linguistics, examining its universality and variation across languages using natural classes similarity metric and multiplicative constraint interaction models.
Learn statistical machine translation techniques, including alignment models, phrase-based systems, and decoding algorithms. Explore cutting-edge approaches to improve translation quality.
Explore vulnerabilities in sequential machine learning and challenge assumptions about imperceptible attacks on image classification through human behavioral studies.
Explore multimodal communication in human-machine interactions using a 3D simulation platform. Learn about VoxML modeling language for encoding objects and actions in shared task environments.
Explore how humans make successful generalizations from limited evidence through Bayesian inferences constrained by implicit domain theories, with applications to machine learning.
Explore how Unbabel combines AI and crowdsourcing to deliver fast, high-quality translations at scale, breaking down language barriers between companies and customers.
Explore techniques to enhance speech translation by addressing accent-related confusions, named entity recognition, and semantics-based disambiguation for improved accuracy and understanding.
Explore innovative approaches to audio deep learning, addressing practical challenges in efficiency, unsupervised learning, and resource-limited scenarios for enhanced audio processing and source separation.
Explore neural models for vision-language understanding, focusing on End-to-End Module Networks and dense video captioning. Learn about interpretable, compositional approaches to visual question answering.
Explore probabilistic dependency grammars for modeling human syntax, addressing linguistic phenomena beyond context-free grammars and discussing generative models and parsing algorithms.
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