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Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations.
A Domain-specific Approach to Heterogeneous Parallelism
H. Chafi, A. K. Sujeeth, K. J. Brown, H. Lee, A. R. Atreya, and K. Olukotun · 2011
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Mesos: A platform for fine-grained resource sharing in the data center., 2011
B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. H. Katz, S. Shenker, and I. Stoica · 2011
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HogWild!: A lock-free approach to parallelizing stochastic gradient descent
B. Recht, C. Re, S. Wright, and F. Niu · 2011
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Worst-case optimal join algorithms
H. Q. Ngo, E. Porat, C. Ré, and A. Rudra · 2012
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Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing
M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker, and I. Stoica · 2012
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Machine learning: The high interest credit card of technical debt
D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, and M. Young · 2014
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Taming the wild: A unified analysis of HogWild!-style algorithms
C. M. De Sa, C. Zhang, K. Olukotun, and C. Ré · 2015
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What we can learn from the epic failure of google flu trends
D. Lazer and R. Kennedy · 2015
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EmptyHeaded: A relational engine for graph processing
C. R. Aberger, S. Tu, K. Olukotun, and C. Ré · 2016
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DeepDive: Declarative knowledge base construction
C. De Sa, A. Ratner, C. Ré, J. Shin, F. Wang, S. Wu, and C. Zhang · 2016
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Recurrence width for structured dense matrix vector multiplication
A. Gu, R. Puttagunta, C. Ré, and A. Rudra · 2016
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Omnivore: An optimizer for multi-device deep learning on CPUs and GPUs
S. Hadjis, C. Zhang, I. Mitliagkas, and C. Ré · 2016
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Data Programming: Creating large training sets, quickly
A. J. Ratner, C. M. De Sa, S. Wu, D. Selsam, and C. Ré · 2016
Cited alongside, same era.
Learning the structure of generative models without labeled data
S. H. Bach, B. D. He, A. Ratner, and C. Ré · 2017
Cited alongside, same era.
Weld: A common runtime for high performance data analytics
S. Palkar, J. J. Thomas, A. Shanbhag, D. Narayanan, H. Pirk, M. Schwarzkopf, S. Amarasinghe, and M. Zaharia · 2017
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Plasticine: A reconfigurable architecture for parallel patterns
R. Prabhakar, Y. Zhang, D. Koeplinger, M. Feldman, T. Zhao, S. Hadjis, A. P. abd Christos Kozyrakis, and K. Olukotun · 2017
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Snorkel: Fast training set generation for information extraction
A. J. Ratner, S. H. Bach, H. E. Ehrenberg, and C. Ré · 2017
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SLiMFast: Guaranteed results for data fusion and source reliability
T. Rekatsinas, M. Joglekar, H. Garcia-Molina, A. Parameswaran, and C. Ré · 2017
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Asap: Automatic smoothing for attention prioritization in streaming time series visualization
K. Rong and P. Bailis · 2017
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P. Bailis, E. Gan, S. Madden, D. Narayanan, K. Rong, and S. Suri · 2017
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Prioritizing Attention in Fast Data: Principles and Promise
P. Bailis, E. Gan, K. Rong, and S. Suri · 2017
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Scalable Kernel Density Classification via Threshold-Based Pruning
Edward Gan and Peter Bailis · 2017
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Optimizing deep cnn-based queries over video streams at scale
D. Kang, J. Emmons, F. Abuzaid, P. Bailis, and M. Zaharia · 2017
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Understanding and optimizing asynchronous low-precision stochastic gradient descent
C. D. Sa, M. Feldman, C. Ré, and K. Olukotun · 2017
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Flipper: A systematic approach to debugging training sets
P. Varma, D. Iter, C. De Sa, and C. Ré · 2017
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Socratic learning: Correcting misspecified generative models using discriminative models
P. Varma, R. Yu, D. Iter, C. De Sa, and C. Ré · 2017
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Splinter: Practical private queries on public data
F. Wang, C. Yun, S. Goldwasser, V. Vaikuntanathan, and M. Zaharia · 2017
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