2016 - Fellow of the Royal Academy of Engineering (UK)
His primary areas of study are Artificial intelligence, Speech recognition, Pattern recognition, Algorithm and Information retrieval. The various areas that Mark Sandler examines in his Artificial intelligence study include Network architecture and Natural language processing. Mark Sandler has included themes like Time–frequency analysis, Transcription, Energy, Signal processing and Audio signal processing in his Speech recognition study.
His Algorithm research incorporates themes from Scale-invariant feature transform, Image processing, Top-hat transform, S transform and Line segment. His Information retrieval research is multidisciplinary, relying on both World Wide Web and Hierarchy. His study in Object detection is interdisciplinary in nature, drawing from both Mobile architecture, Bottleneck, Mobile device and Task.
His primary scientific interests are in Artificial intelligence, Speech recognition, Algorithm, Pattern recognition and Electronic engineering. His Artificial intelligence research includes elements of Machine learning, Computer vision and Natural language processing. He works mostly in the field of Speech recognition, limiting it down to topics relating to Music information retrieval and, in certain cases, Multimedia.
Mark Sandler studies Pattern recognition, focusing on Segmentation in particular. He interconnects Pulse-width modulation, Delta-sigma modulation and Modulation in the investigation of issues within Electronic engineering. His Delta-sigma modulation research incorporates elements of Dither and Control theory.
Mark Sandler focuses on Artificial intelligence, Algorithm, Artificial neural network, Pattern recognition and Machine learning. His Natural language processing research extends to the thematically linked field of Artificial intelligence. His work in the fields of Algorithm, such as Source separation, intersects with other areas such as Visibility graph.
In the subject of general Artificial neural network, his work in Gradient descent is often linked to Mobile processor, thereby combining diverse domains of study. His research integrates issues of Segmentation, Image segmentation, Mobile device and Search algorithm in his study of Object detection. His work deals with themes such as Mobile architecture and Bottleneck, which intersect with Image segmentation.
His primary areas of investigation include Artificial intelligence, Artificial neural network, Algorithm, Object detection and Pattern recognition. His Artificial neural network study incorporates themes from Energy consumption, Real-time computing, Inference and Speedup. He combines subjects such as Kernel, Feature, Robust statistics, Transfer of learning and Transfer with his study of Algorithm.
His Object detection study integrates concerns from other disciplines, such as Segmentation, Image segmentation and Pyramid. Within one scientific family, Mark Sandler focuses on topics pertaining to Network architecture under Segmentation, and may sometimes address concerns connected to Next-generation network. His Image segmentation research includes themes of Mobile architecture and Bottleneck.
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler;Andrew Howard;Menglong Zhu;Andrey Zhmoginov.
computer vision and pattern recognition (2018)
Searching for MobileNetV3
Andrew Howard;Ruoming Pang;Hartwig Adam;Quoc Le.
international conference on computer vision (2019)
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan;Bo Chen;Ruoming Pang;Vijay Vasudevan.
computer vision and pattern recognition (2019)
A tutorial on onset detection in music signals
J.P. Bello;L. Daudet;S. Abdallah;C. Duxbury.
IEEE Transactions on Speech and Audio Processing (2005)
Searching for MobileNetV3.
Andrew Howard;Mark Sandler;Grace Chu;Liang-Chieh Chen.
arXiv: Computer Vision and Pattern Recognition (2019)
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation
Andrew Howard;Andrey Zhmoginov;Liang-Chieh Chen;Mark Sandler.
The Music Ontology.
Yves Raimond;Samer A. Abdallah;Mark B. Sandler;Frederick Giasson.
international symposium/conference on music information retrieval (2007)
Sonic visualiser: an open source application for viewing, analysing, and annotating music audio files
Chris Cannam;Christian Landone;Mark Sandler.
acm multimedia (2010)
Convolutional recurrent neural networks for music classification
Keunwoo Choi;Gyorgy Fazekas;Mark Sandler;Kyunghyun Cho.
international conference on acoustics, speech, and signal processing (2017)
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
Tien-Ju Yang;Andrew G. Howard;Bo Chen;Xiao Zhang.
european conference on computer vision (2018)
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