Bryan Catanzaro spends much of his time researching Artificial intelligence, Deep learning, CUDA, Speech recognition and Parallel computing. His work in Artificial intelligence tackles topics such as Pattern recognition which are related to areas like Image resolution. His Deep learning research focuses on Artificial neural network and how it connects with End-to-end principle and Transcription.
His research integrates issues of Computer architecture, High-level programming language, Distributed computing and Graphics in his study of CUDA. In his work, Audio mining and Speech analytics is strongly intertwined with Machine learning, which is a subfield of Speech recognition. The concepts of his Data parallelism study are interwoven with issues in Data type, Scalability, System software, Task parallelism and Programming paradigm.
His primary areas of investigation include Artificial intelligence, Deep learning, Machine learning, Parallel computing and Speech recognition. The Artificial intelligence study combines topics in areas such as Computer vision and Pattern recognition. His research investigates the link between Deep learning and topics such as Recurrent neural network that cross with problems in Synchronization.
His Machine learning research is multidisciplinary, incorporating perspectives in Range and Inference. His work deals with themes such as Computer architecture, Programming paradigm and Implementation, which intersect with Parallel computing. His Speech recognition research focuses on End-to-end principle and how it relates to Latency.
Bryan Catanzaro mainly investigates Artificial intelligence, Language model, Generative grammar, Machine learning and Computer vision. His work often combines Artificial intelligence and Generator studies. His Perplexity study, which is part of a larger body of work in Language model, is frequently linked to Quality, bridging the gap between disciplines.
His Generative grammar research includes themes of Speech recognition, Speech synthesis and Interpolation. The study incorporates disciplines such as Segmentation and Inference in addition to Machine learning. His work on Object as part of general Computer vision study is frequently linked to Focus, therefore connecting diverse disciplines of science.
His main research concerns Artificial intelligence, Speech recognition, Autoregressive model, Speech synthesis and Machine learning. He undertakes interdisciplinary study in the fields of Artificial intelligence and Rhythm through his works. His work in the fields of Speech recognition, such as Spectrogram, overlaps with other areas such as Emotive and Variety.
Bryan Catanzaro has researched Speech synthesis in several fields, including Stress and Interpolation. The various areas that Bryan Catanzaro examines in his Machine learning study include Segmentation and Inference. His Language model research is multidisciplinary, relying on both Question answering, Text corpus, F1 score and Set.
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The Landscape of Parallel Computing Research: A View from Berkeley
Krste Asanovic;Ras Bodik;Bryan Christopher Catanzaro;Joseph James Gebis.
Deep speech 2: end-to-end speech recognition in English and mandarin
Dario Amodei;Sundaram Ananthanarayanan;Rishita Anubhai;Jingliang Bai.
international conference on machine learning (2016)
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Ting-Chun Wang;Ming-Yu Liu;Jun-Yan Zhu;Andrew Tao.
computer vision and pattern recognition (2018)
cuDNN: Efficient Primitives for Deep Learning
Sharan Chetlur;Cliff Woolley;Philippe Vandermersch;Jonathan Cohen.
arXiv: Neural and Evolutionary Computing (2014)
Deep Speech: Scaling up end-to-end speech recognition
Awni Y. Hannun;Carl Case;Jared Casper;Bryan Catanzaro.
arXiv: Computation and Language (2014)
Image Inpainting for Irregular Holes Using Partial Convolutions
Guilin Liu;Fitsum A. Reda;Kevin J. Shih;Ting-Chun Wang.
european conference on computer vision (2018)
Deep learning with COTS HPC systems
Adam Coates;Brody Huval;Tao Wang;David Wu.
international conference on machine learning (2013)
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation
Andreas Klöckner;Nicolas Pinto;Yunsup Lee;Bryan Catanzaro.
parallel computing (2012)
Waveglow: A Flow-based Generative Network for Speech Synthesis
Ryan Prenger;Rafael Valle;Bryan Catanzaro.
international conference on acoustics speech and signal processing (2019)
Ting-Chun Wang;Ming-Yu Liu;Jun-Yan Zhu;Guilin Liu.
neural information processing systems (2018)
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