3,318 research outputs found
An ant colony based model to optimize parameters in industrial vision
Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful
Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix
Recent developments in the field of deep learning have motivated many
researchers to apply these methods to problems in quantum information. Torlai
and Melko first proposed a decoder for surface codes based on neural networks.
Since then, many other researchers have applied neural networks to study a
variety of problems in the context of decoding. An important development in
this regard was due to Varsamopoulos et al. who proposed a two-step decoder
using neural networks. Subsequent work of Maskara et al. used the same concept
for decoding for various noise models. We propose a similar two-step neural
decoder using inverse parity-check matrix for topological color codes. We show
that it outperforms the state-of-the-art performance of non-neural decoders for
independent Pauli errors noise model on a 2D hexagonal color code. Our final
decoder is independent of the noise model and achieves a threshold of .
Our result is comparable to the recent work on neural decoder for quantum error
correction by Maskara et al.. It appears that our decoder has significant
advantages with respect to training cost and complexity of the network for
higher lengths when compared to that of Maskara et al.. Our proposed method can
also be extended to arbitrary dimension and other stabilizer codes.Comment: 12 pages, 12 figures, 2 tables, submitted to the 2019 IEEE
International Symposium on Information Theor
HARL: Hierarchical Adaptive Reinforcement Learning Based Auto Scheduler for Neural Networks
To efficiently perform inference with neural networks, the underlying tensor
programs require sufficient tuning efforts before being deployed into
production environments. Usually, enormous tensor program candidates need to be
sufficiently explored to find the one with the best performance. This is
necessary to make the neural network products meet the high demand of
real-world applications such as natural language processing, auto-driving, etc.
Auto-schedulers are being developed to avoid the need for human intervention.
However, due to the gigantic search space and lack of intelligent search
guidance, current auto-schedulers require hours to days of tuning time to find
the best-performing tensor program for the entire neural network.
In this paper, we propose HARL, a reinforcement learning (RL) based
auto-scheduler specifically designed for efficient tensor program exploration.
HARL uses a hierarchical RL architecture in which learning-based decisions are
made at all different levels of search granularity. It also automatically
adjusts exploration configurations in real-time for faster performance
convergence. As a result, HARL improves the tensor operator performance by 22%
and the search speed by 4.3x compared to the state-of-the-art auto-scheduler.
Inference performance and search speed are also significantly improved on
end-to-end neural networks
Evolutionary Augmentation Policy Optimization for Self-supervised Learning
Self-supervised Learning (SSL) is a machine learning algorithm for
pretraining Deep Neural Networks (DNNs) without requiring manually labeled
data. The central idea of this learning technique is based on an auxiliary
stage aka pretext task in which labeled data are created automatically through
data augmentation and exploited for pretraining the DNN. However, the effect of
each pretext task is not well studied or compared in the literature. In this
paper, we study the contribution of augmentation operators on the performance
of self supervised learning algorithms in a constrained settings. We propose an
evolutionary search method for optimization of data augmentation pipeline in
pretext tasks and measure the impact of augmentation operators in several SOTA
SSL algorithms. By encoding different combination of augmentation operators in
chromosomes we seek the optimal augmentation policies through an evolutionary
optimization mechanism. We further introduce methods for analyzing and
explaining the performance of optimized SSL algorithms. Our results indicate
that our proposed method can find solutions that outperform the accuracy of
classification of SSL algorithms which confirms the influence of augmentation
policy choice on the overall performance of SSL algorithms. We also compare
optimal SSL solutions found by our evolutionary search mechanism and show the
effect of batch size in the pretext task on two visual datasets
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