Search Results - "Mazumder, Mark"

Widening Access to Applied Machine Learning with TinyML by Vijay Janapa Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Lara Suzuki, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart, Dustin Tingley

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Guaranteeing spoof-resilient multi-robot networks by Gil, Stephanie, Kumar, Swarun, Mazumder, Mark, Katabi, Dina, Rus, Daniela

“…Multi-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective…”
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Toward an Autonomous Aerial Survey and Planning System for Humanitarian Aid and Disaster Response by Allen, Ross, Mazumder, Mark

“…In this paper we propose an integrated system concept for autonomously surveying and planning emergency response for areas impacted by natural disasters…”
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Safe Client/Server Web Development with Haskell by Mazumder, Mark, Braje, Timothy

“…We demonstrate how two Haskell libraries - Reflex-Dom and Servant - provide a powerful and complete web development framework which uses typechecking to…”
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Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers by Prakash, Shvetank, Stewart, Matthew, Banbury, Colby, Mazumder, Mark, Warden, Pete, Plancher, Brian, Reddi, Vijay Janapa

“…The sustained growth of carbon emissions and global waste elicits significant sustainability concerns for our environment's future. The growing Internet of…”
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Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots by Neuman, Sabrina M., Plancher, Brian, Duisterhof, Bardienus P., Krishnan, Srivatsan, Banbury, Colby, Mazumder, Mark, Prakash, Shvetank, Jabbour, Jason, Faust, Aleksandra, de Croon, Guido C.H.E., Reddi, Vijay Janapa

“…Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is…”
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Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi by Wang, Weiying, Jadhav, Ninad, Vohs, Paul, Hughes, Nathan, Mazumder, Mark, Gil, Stephanie

“…International Symposium on Robotics Research (ISRR), Hanoi, 2019 We present a novel framework for collaboration amongst a team of robots performing Pose Graph…”
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Few-Shot Keyword Spotting in Any Language by Mazumder, Mark, Banbury, Colby, Meyer, Josh, Warden, Pete, Reddi, Vijay Janapa

“…Proc. Interspeech 2021 We introduce a few-shot transfer learning method for keyword spotting in any language. Leveraging open speech corpora in nine languages,…”
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XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse by Kwon, Hyoukjun, Nair, Krishnakumar, Seo, Jamin, Yik, Jason, Mohapatra, Debabrata, Zhan, Dongyuan, Song, Jinook, Capak, Peter, Zhang, Peizhao, Vajda, Peter, Banbury, Colby, Mazumder, Mark, Lai, Liangzhen, Sirasao, Ashish, Krishna, Tushar, Khaitan, Harshit, Chandra, Vikas, Reddi, Vijay Janapa

“…Real-time multi-task multi-model (MTMM) workloads, a new form of deep learning inference workloads, are emerging for applications areas like extended reality…”
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The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage by Galvez, Daniel, Diamos, Greg, Ciro, Juan, Cerón, Juan Felipe, Achorn, Keith, Gopi, Anjali, Kanter, David, Lam, Maximilian, Mazumder, Mark, Reddi, Vijay Janapa

“…The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and…”
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Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots by Neuman, Sabrina M, Plancher, Brian, Duisterhof, Bardienus P, Krishnan, Srivatsan, Banbury, Colby, Mazumder, Mark, Prakash, Shvetank, Jabbour, Jason, Faust, Aleksandra, de Croon, Guido C. H. E, Reddi, Vijay Janapa

“…Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is…”
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DataPerf: Benchmarks for Data-Centric AI Development by Mazumder, Mark, Banbury, Colby, Yao, Xiaozhe, Karlaš, Bojan, Rojas, William Gaviria, Diamos, Sudnya, Diamos, Greg, He, Lynn, Parrish, Alicia, Kirk, Hannah Rose, Quaye, Jessica, Rastogi, Charvi, Kiela, Douwe, Jurado, David, Kanter, David, Mosquera, Rafael, Ciro, Juan, Aroyo, Lora, Acun, Bilge, Chen, Lingjiao, Raje, Mehul Smriti, Bartolo, Max, Eyuboglu, Sabri, Ghorbani, Amirata, Goodman, Emmett, Inel, Oana, Kane, Tariq, Kirkpatrick, Christine R, Kuo, Tzu-Sheng, Mueller, Jonas, Thrush, Tristan, Vanschoren, Joaquin, Warren, Margaret, Williams, Adina, Yeung, Serena, Ardalani, Newsha, Paritosh, Praveen, Bat-Leah, Lilith, Zhang, Ce, Zou, James, Wu, Carole-Jean, Coleman, Cody, Ng, Andrew, Mattson, Peter, Reddi, Vijay Janapa

“…Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth,…”
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Widening Access to Applied Machine Learning with TinyML by Reddi, Vijay Janapa, Plancher, Brian, Kennedy, Susan, Moroney, Laurence, Warden, Pete, Agarwal, Anant, Banbury, Colby, Banzi, Massimo, Bennett, Matthew, Brown, Benjamin, Chitlangia, Sharad, Ghosal, Radhika, Grafman, Sarah, Jaeger, Rupert, Krishnan, Srivatsan, Lam, Maximilian, Leiker, Daniel, Mann, Cara, Mazumder, Mark, Pajak, Dominic, Ramaprasad, Dhilan, Smith, J. Evan, Stewart, Matthew, Tingley, Dustin

“…Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources…”
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MLPerf Mobile Inference Benchmark by Reddi, Vijay Janapa, Kanter, David, Mattson, Peter, Duke, Jared, Nguyen, Thai, Chukka, Ramesh, Shiring, Ken, Tan, Koan-Sin, Charlebois, Mark, Chou, William, El-Khamy, Mostafa, Hong, Jungwook, John, Tom St, Trinh, Cindy, Buch, Michael, Mazumder, Mark, Markovic, Relia, Atta, Thomas, Cakir, Fatih, Charkhabi, Masoud, Chen, Xiaodong, Chiang, Cheng-Ming, Dexter, Dave, Heo, Terry, Schmuelling, Gunther, Shabani, Maryam, Zika, Dylan

“…This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices…”
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