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The quest for determinism in machine learning has disproportionately focused on characterizing the impact of noise introduced by algorithmic design choices.
What does research reproducibility mean?
S. N. Goodman, D. Fanelli, and J. P. A. Ioannidis · 1946
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Why systolic architectures?
H. T. Kung · 1982
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Replicability is not reproducibility: Nor is it good science
C. Drummond · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Anti-distillation: Improving reproducibility of deep networks
G. I. Shamir and L. Coviello · 2010
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Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease
N. R. Council · 2011
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Improving neural networks by preventing co-adaptation of feature detectors, 2012
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2012
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An evaluation of the software system dependency of a global atmospheric model
S.-Y. Hong, M.-S. Koo, J. Jang, J.-E. E. Kim, H. Park, M.-S. Joh, J.-H. Kang, and T.-J. Oh · 2013
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GPUDet: a deterministic GPU architecture
H. Jooybar, W. W. L. Fung, M. O’Connor, J. Devietti, and T. M. Aamodt · 2013
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Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus · 2013
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cuDNN: Efficient primitives for deep learning
S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer · 2014
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Scaling distributed machine learning with the parameter server
M. Li, D. G. Andersen, J. W. Park, A. J. Smola, A. Ahmed, V. Josifovski, J. Long, E. J. Shekita, and B. Su · 2014
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Docker: Lightweight linux containers for consistent development and deployment
D. Merkel · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Rethinking the inception architecture for computer vision, 2015
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
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Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. A. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2016
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Repeatability in computer systems research
C. Collberg and T. A. Proebsting · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Jupyter notebooks ? a publishing format for reproducible computational workflows
T. Kluyver, B. Ragan-Kelley, F. Pérez, B. Granger, M. Bussonnier, J. Frederic, K. Kelley, J. Hamrick, J. Grout, S. Corlay, P. Ivanov, D. Avila, S. Abdalla, C. Willing, and J. development team · 2016
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Launch and iterate: Reducing prediction churn
M. Milani Fard, Q. Cormier, K. Canini, and M. Gupta · 2016
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NVIDIA Tesla P100, 2016
NVIDIA · 2016
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Facilitating reproducible research by investigating computational metadata
P. Thavasimani and P. Missier · 2016
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Cut, paste and learn: Surprisingly easy synthesis for instance detection, 2017
D. Dwibedi, I. Misra, and M. Hebert · 2017
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Deep reinforcement learning that matters
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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On the compatibility of privacy and fairness
R. Cummings, V. Gupta, D. Kimpara, and J. Morgenstern · 2019
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What Do Compressed Deep Neural Networks Forget?
S. Hooker, A. Courville, G. Clark, Y. Dauphin, and A. Frome · 2019
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Out-of-the-box reproducibility: A survey of machine learning platforms
R. Isdahl and O. E. Gundersen · 2019
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On model stability as a function of random seed
P. Madhyastha and R. Jain · 2019
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Reproducibility in machine learning for health
M. B. A. McDermott, S. Wang, N. Marinsek, R. Ranganath, M. Ghassemi, and L. Foschini · 2019
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Knowledge base completion: Baselines strike back
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Regularization for deep learning: A taxonomy, 2017
J. Kukačka, V. Golkov, and D. Cremers · 2017
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Regularizing and optimizing lstm language models, 2017
S. Merity, N. S. Keskar, and R. Socher · 2017
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Technical report, U.S. Department of Transportation, National Highway Traffic, Tesla Crash Preliminary Evaluation Report Safety Administration
NHTSA · 2017
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NVIDIA TESLA V100 GPU ARCHITECTURE, 2017
NVIDIA · 2017
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Dlpaper2code: Auto-generation of code from deep learning research papers, 2017
A. Sethi, A. Sankaran, N. Panwar, S. Khare, and S. Mani · 2017
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L. Oakden-Rayner, J. Dunnmon, G. Carneiro, and C. Ré · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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A step toward quantifying independently reproducible machine learning research, 2019
E. Raff · 2019
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Measuring the effects of data parallelism on neural network training, 2019
C. J. Shallue, J. Lee, J. Antognini, J. Sohl-Dickstein, R. Frostig, and G. E. Dahl · 2019
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Challenges for the repeatability of deep learning models
S. S. Alahmari, D. B. Goldgof, P. R. Mouton, and L. O. Hall · 2020
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Beyond point estimate: Inferring ensemble prediction variation from neuron activation strength in recommender systems, 2020
Z. Chen, Y. Wang, D. Lin, D. Z. Cheng, L. Hong, E. H. Chi, and C. Cui · 2020
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Deterministic atomic buffering
Y. Chou, C. Ng, S. Cattell, J. Intan, M. D. Sinclair, J. Devietti, T. G. Rogers, and T. M. Aamodt · 2020
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Underspecification presents challenges for credibility in modern machine learning, 2020
A. D’Amour, K. Heller, D. Moldovan, B. Adlam, B. Alipanahi, A. Beutel, C. Chen, J. Deaton, J. Eisenstein, M. D. Hoffman, F. Hormozdiari, N. Houlsby, S. Hou, G. Jerfel, A. Karthikesalingam, M. Lucic, Y. Ma, C. McLean, D. Mincu, A. Mitani, A. Montanari, Z. Nado, V. Natarajan, C. Nielson, T. F. Osborne, R. Raman, K. Ramasamy, R. Sayres, J. Schrouff, M. Seneviratne, S. Sequeira, H. Suresh, V. Veitch, M. Vladymyrov, X. Wang, K. Webster, S. Yadlowsky, T. Yun, X. Zhai, and D. Sculley · 2020
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Characterising bias in compressed models, 2020
S. Hooker, N. Moorosi, G. Clark, S. Bengio, and E. Denton · 2020
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Distributed training of deep learning models: A taxonomic perspective
M. Langer, Z. He, W. Rahayu, and Y. Xue · 2020
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Problems and opportunities in training deep learning software systems: An analysis of variance
H. V. Pham, S. Qian, J. Wang, T. Lutellier, J. Rosenthal, L. Tan, Y. Yu, and N. Nagappan · 2020
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Zero: memory optimizations toward training trillion parameter models
S. Rajbhandari, J. Rasley, O. Ruwase, and Y. He · 2020
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Smooth activations and reproducibility in deep networks, 2020
G. I. Shamir, D. Lin, and L. Coviello · 2020
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Efficientnet: Rethinking model scaling for convolutional neural networks, 2020
M. Tan and Q. V. Le · 2020
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Keep the gradients flowing: Using gradient flow to study sparse network optimization, 2021
K. ab Tessera, S. Hooker, and B. Rosman · 2021
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Synthesizing irreproducibility in deep networks
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We need to talk about random splits
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Nondeterminism and instability in neural network optimization, 2021
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