2,968 research outputs found
The effects of quantization on the backpropagation learning
The effects of the quantization of the parameters of a learning machine are discussed. The learning coefficient should be as small as possible for a better estimate of parameters. On the other hand, when the parameters are quantized, it should be relatively larger in order to avoid the paralysis of learning originated from the quantization. How to choose the learning coefficient is given in this paper from the statistical point of view
Fixed-Point Performance Analysis of Recurrent Neural Networks
Recurrent neural networks have shown excellent performance in many
applications, however they require increased complexity in hardware or software
based implementations. The hardware complexity can be much lowered by
minimizing the word-length of weights and signals. This work analyzes the
fixed-point performance of recurrent neural networks using a retrain based
quantization method. The quantization sensitivity of each layer in RNNs is
studied, and the overall fixed-point optimization results minimizing the
capacity of weights while not sacrificing the performance are presented. A
language model and a phoneme recognition examples are used
Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
Ren et al. recently introduced a method for aggregating multiple decision
trees into a strong predictor by interpreting a path taken by a sample down
each tree as a binary vector and performing linear regression on top of these
vectors stacked together. They provided experimental evidence that the method
offers advantages over the usual approaches for combining decision trees
(random forests and boosting). The method truly shines when the regression
target is a large vector with correlated dimensions, such as a 2D face shape
represented with the positions of several facial landmarks. However, we argue
that their basic method is not applicable in many practical scenarios due to
large memory requirements. This paper shows how this issue can be solved
through the use of quantization and architectural changes of the predictor that
maps decision tree-derived encodings to the desired output.Comment: BMVC Newcastle 201
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
For the efficient compensation of fiber nonlinearity, one of the guiding
principles appears to be: fewer steps are better and more efficient. We
challenge this assumption and show that carefully designed multi-step
approaches can lead to better performance-complexity trade-offs than their
few-step counterparts.Comment: 4 pages, 3 figures, This is a preprint of a paper submitted to the
2019 European Conference on Optical Communicatio
On the efficient representation and execution of deep acoustic models
In this paper we present a simple and computationally efficient quantization
scheme that enables us to reduce the resolution of the parameters of a neural
network from 32-bit floating point values to 8-bit integer values. The proposed
quantization scheme leads to significant memory savings and enables the use of
optimized hardware instructions for integer arithmetic, thus significantly
reducing the cost of inference. Finally, we propose a "quantization aware"
training process that applies the proposed scheme during network training and
find that it allows us to recover most of the loss in accuracy introduced by
quantization. We validate the proposed techniques by applying them to a long
short-term memory-based acoustic model on an open-ended large vocabulary speech
recognition task.Comment: Accepted conference paper: "The Annual Conference of the
International Speech Communication Association (Interspeech), 2016
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Adversarial examples have emerged as a significant threat to machine learning
algorithms, especially to the convolutional neural networks (CNNs). In this
paper, we propose two quantization-based defense mechanisms, Constant
Quantization (CQ) and Trainable Quantization (TQ), to increase the robustness
of CNNs against adversarial examples. CQ quantizes input pixel intensities
based on a "fixed" number of quantization levels, while in TQ, the quantization
levels are "iteratively learned during the training phase", thereby providing a
stronger defense mechanism. We apply the proposed techniques on undefended CNNs
against different state-of-the-art adversarial attacks from the open-source
\textit{Cleverhans} library. The experimental results demonstrate 50%-96% and
10%-50% increase in the classification accuracy of the perturbed images
generated from the MNIST and the CIFAR-10 datasets, respectively, on commonly
used CNN (Conv2D(64, 8x8) - Conv2D(128, 6x6) - Conv2D(128, 5x5) - Dense(10) -
Softmax()) available in \textit{Cleverhans} library
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