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We propose a method of training quantization thresholds (TQT) for uniform symmetric quantizers using standard backpropagation and gradient descent.
Non-parametric information-theoretic measures of one-dimensional distribution functions from continuous time series
D’Alberto, P. and Dasdan, A · 2009
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Lecture 6a overview of mini-batch gradient descent (2012)
Hinton, G., Srivastava, N., and Swersky, K · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2015
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Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., and Bengio, Y · 2016
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Li, F., Zhang, B., and Liu, B · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A · 2016
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Yolo9000: Better, faster, stronger
Redmon, J. and Farhadi, A · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Nice: Noise injection and clamping estimation for neural network quantization
Baskin, C., Liss, N., Chai, Y., Zheltonozhskii, E., Schwartz, E., Giryes, R., Mendelson, A., and Bronstein, A. M · 2018
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Pact: Parameterized clipping activation for quantized neural networks
Choi, J., Wang, Z., Venkataramani, S., Chuang, P. I.-J., Srinivasan, V., and Gopalakrishnan, K · 2018
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Fast adjustable threshold for uniform neural network quantization
Goncharenko, A., Denisov, A., Alyamkin, S., and Terentev, E · 2018
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Darknet to tensorflow (DW2TF)
Hao, Y. and Jain, S. R · 2018
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Learning to quantize deep networks by optimizing quantization intervals with task loss
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Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., and Zou, Y · 2016
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Zhu, C., Han, S., Mao, H., and Dally, W. J · 2016
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Deep learning with low precision by half-wave gaussian quantization
Cai, Z., He, X., Sun, J., and Vasconcelos, N · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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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
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8-bit inference with tensorrt
Migacz, S · 2017
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Wrpn: wide reduced-precision networks
Mishra, A., Nurvitadhi, E., Cook, J. J., and Marr, D · 2017
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Gemmlowp: Efficient handling of offsets
Jacob, B. et al
Cited in the paper.
Jung, S., Son, C., Lee, S., Son, J., Kwak, Y., Han, J.-J., Hwang, S. J., and Choi, C · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Krishnamoorthi, R · 2018
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Discovering low-precision networks close to full-precision networks for efficient embedded inference
McKinstry, J. L., Esser, S. K., Appuswamy, R., Bablani, D., Arthur, J. V., Yildiz, I. B., and Modha, D. S · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
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Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Zhang, D., Yang, J., Ye, D., and Hua, G · 2018
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Learned step size quantization
Esser, S. K., McKinstry, J. L., Bablani, D., Appuswamy, R., and Modha, D. S · 2019
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