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Speed/accuracy trade-offs for modern convolutional object detectors.

J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy. CoRR, (2016)

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Speed/accuracy trade-offs for modern convolutional object detectors.J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama and 1 other author(s). CoRR, (2016)Im2Calories: Towards an Automated Mobile Vision Food Diary.A. Myers, N. Johnston, V. Rathod, A. Korattikara, A. Gorban, N. Silberman, S. Guadarrama, G. Papandreou, J. Huang, and K. Murphy. ICCV, page 1233-1241. IEEE Computer Society, (2015)Sequential Tests for Large-Scale Learning.A. Korattikara, Y. Chen, and M. Welling. Neural Computation, 28 (1): 45-70 (2016)From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following.J. Fu, A. Korattikara, S. Levine, and S. Guadarrama. ICLR (Poster), OpenReview.net, (2019)Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC.S. Ahn, A. Korattikara, N. Liu, S. Rajan, and M. Welling. KDD, page 9-18. ACM, (2015)Measuring the Reliability of Reinforcement Learning Algorithms.S. Chan, S. Fishman, J. Canny, A. Korattikara, and S. Guadarrama. CoRR, (2019)Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget.A. Balan, Y. Chen, and M. Welling. ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 181-189. JMLR.org, (2014)Austerity in MCMC Land: Cutting the Metropolis-Hastings BudgetA. Balan, Y. Chen, and M. Welling. CoRR, (2013)Statistical Optimization of Non-Negative Matrix Factorization.A. Balan, L. Boyles, M. Welling, J. Kim, and H. Park. AISTATS, volume 15 of JMLR Proceedings, page 128-136. JMLR.org, (2011)Statistical Tests for Optimization Efficiency.L. Boyles, A. Balan, D. Ramanan, and M. Welling. NIPS, page 2196-2204. (2011)

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Speed/accuracy trade-offs for modern convolutional object detectors.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Speed/accuracy trade-offs for modern convolutional object detectors.

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Speed/accuracy trade-offs for modern convolutional object detectors.