Fetching the paper…
Reading the bibliography…
The popularity and widespread use of pruning and quantization is driven by the severe resource constraints of deploying deep neural networks to environments with strict latency, memory and energy requirements.
The state of sparsity in deep neural networks
Gale, T., Elsen, E., and Hooker, S · 1902
Earlier work this paper cites.
Towards compact and robust deep neural networks
Sehwag, V., Wang, S., Mittal, P., and Jana, S · 1906
Earlier work this paper cites.
What Do Compressed Deep Neural Networks Forget?
Hooker, S., Courville, A., Clark, G., Dauphin, Y., and Frome, A · 1911
Earlier work this paper cites.
What Do Compressed Deep Neural Networks Forget?
Hooker, S., Courville, A., Clark, G., Dauphin, Y., and Frome, A · 1911
Earlier work this paper cites.
Optimal brain damage
Cun, Y. L., Denker, J. S., and Solla, S. A · 1990
Earlier work this paper cites.
Dynamically scaled fixed point arithmetic
Williamson, D · 1991
Earlier work this paper cites.
Sparse connection and pruning in large dynamic artificial neural networks, 1997
Ström, N · 1997
Earlier work this paper cites.
Finding the jaccard median
Chierichetti, F., Kumar, R., Pandey, S., and Vassilvitskii, S · 2010
Earlier work this paper cites.
Improving the speed of neural networks on cpus
Vanhoucke, V., Senior, A., and Mao, M. Z · 2011
Earlier work this paper cites.
Capturing long-tail distributions of object subcategories
Zhu, X., Anguelov, D., and Ramanan, D · 2014
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
On the efficient representation and execution of deep acoustic models
Alvarez, R., Prabhavalkar, R., and Bakhtin, A · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
Earlier work this paper cites.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Hendrycks, D. and Gimpel, K · 2016
Earlier work this paper cites.
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R., Ko, J., M Swetter, S., M Blau, H., and Thrun, S · 2017
Cited alongside, same era.
Word embeddings quantify 100 years of gender and ethnic stereotypes
Garg, N., Schiebinger, L., Jurafsky, D., and Zou, J · 2017
Cited alongside, same era.
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D · 2017
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A. G., Adam, H., and Kalenichenko, D · 2017
Cited alongside, same era.
The deep (learning) transformation of mobile and embedded computing
Lane, N. D. and Warden, P · 2018
Later among the works it cites.
Metric Learning for Novelty and Anomaly Detection
Masana, M., Ruiz, I., Serrat, J., van de Weijer, J., and Lopez, A. M · 2018
Later among the works it cites.
Automated pulmonary nodule detection in ct images using deep convolutional neural networks
Xie, H., Yang, D., Sun, N., Chen, Z., and Zhang, Y · 2018
Later among the works it cites.
Deep learning predicts hip fracture using confounding patient and healthcare variables
Badgeley, M., Zech, J., Oakden-Rayner, L., Glicksberg, B., Liu, M., Gale, W., McConnell, M., Percha, B., and Snyder, T · 2019
Later among the works it cites.
Prototypical examples in deep learning: Metrics, characteristics, and utility, 2019
Carlini, N., Erlingsson, U., and Papernot, N · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Narang, S., Elsen, E., Diamos, G., and Sengupta, S · 2017
Cited alongside, same era.
Technical report, U.S. Department of Transportation, National Highway Traffic, Tesla Crash Preliminary Evaluation Report Safety Administration
NHTSA · 2017
Cited alongside, same era.
Shankar, S., Halpern, Y., Breck, E., Atwood, J., Wilson, J., and Sculley, D · 2017
Cited alongside, same era.
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases
Stock, P. and Cisse, M · 2017
Cited alongside, same era.
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
Veale, M. and Binns, R · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., and Chang, K.-W · 2017
Cited alongside, same era.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Zhu, M. and Gupta, S · 2017
Cited alongside, same era.
Amazon scraps secret ai recruiting tool that showed bias against women
Dastin, J · 2018
Cited alongside, same era.
DeVries, T., Misra, I., Wang, C., and van der Maaten, L · 2019
Later among the works it cites.
Rigging the lottery: Making all tickets winners, 2019
Evci, U., Gale, T., Menick, J., Castro, P. S., and Elsen, E · 2019
Later among the works it cites.
Does learning require memorization? a short tale about a long tail
Feldman, V · 2019
Later among the works it cites.
A face-scanning algorithm increasingly decides whether you deserve the job
Harwell, D · 2019
Later among the works it cites.
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
Oakden-Rayner, L., Dunnmon, J., Carneiro, G., and Ré, C · 2019
Later among the works it cites.
Estimating Example Difficulty using Variance of Gradients
Agarwal, C. and Hooker, S · 2020
Closest in time.
What is the State of Neural Network Pruning?
Blalock, D., Gonzalez Ortiz, J. J., Frankle, J., and Guttag, J · 2020
Closest in time.
Social biases in nlp models as barriers for persons with disabilities
Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., and Denuyl, S. C · 2020
Closest in time.
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Jiang, Z., Zhang, C., Talwar, K., and Mozer, M. C · 2020
Closest in time.
Mlir: A compiler infrastructure for the end of moore’s law, 2020
Lattner, C., Amini, M., Bondhugula, U., Cohen, A., Davis, A., Pienaar, J., Riddle, R., Shpeisman, T., Vasilache, N., and Zinenko, O · 2020
Closest in time.