查看更多>>摘要：Commonly used clustering algorithms typically require user parameters such as the number of clusters to be divided. Density-based algorithms do not have such requirements but are not suitable for high dimensional data. Recent studies have merged the cluster assignment task with deep similarity learning. In this paper, we propose a novel framework to perform dynamic image clustering without prior knowledge of the cluster count. A deep learning model first learns data similarity from scratch, followed by the use of a coordinate learning model to project high dimensional data onto a twodimensional space. A new clustering algorithm, raster clustering, is proposed to evaluate and classify the projected data. This mechanism can be applied in high dimensional data clustering like image data, and it allows the prediction of unseen data in a consistent way without the need for consolidating with training data.
Larsen, Thomas NakkenMeyer, EivindRasheed, AdilSan, Omer...
17页
查看更多>>摘要：Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector. Since the electrification of transport is happening simultaneously, autonomous marine vessels can reduce environmental impact, lower costs, and increase efficiency. Although close monitoring is still required to ensure safety, the ultimate goal is full autonomy. One major milestone is to develop a control system that is versatile enough to handle any weather and encounter that is also robust and reliable. Additionally, the control system must adhere to the International Regulations for Preventing Collisions at Sea (COLREGs) for successful interaction with human sailors. Since the COLREGs were written for the human mind to interpret, they are written in ambiguous prose and therefore not machine-readable or verifiable. Due to these challenges and the wide variety of situations to be tackled, classical model-based approaches prove complicated to implement and computationally heavy. Within machine learning (ML), deep reinforcement learning (DRL) has shown great potential for a wide range of applications. The model-free and self-learning properties of DRL make it a promising candidate for autonomous vessels. In this work, a subset of the COLREGs is incorporated into a DRL-based path following and obstacle avoidance system using collision risk theory. The resulting autonomous agent dynamically interpolates between path following and COLREG-compliant collision avoidance in the training scenario, isolated encounter situations, and AIS-based simulations of real-world scenarios. (C) 2022 The Author(s). Published by Elsevier Ltd.
查看更多>>摘要：Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance features can be identified using the Laplacian score criterion, which evaluates the importance of a given feature via its consistency with the Graph Laplacians' leading eigenvectors. We demonstrate that in the presence of large numbers of nuisance features, the Laplacian must be computed on the subset of selected features rather than on the complete feature set. To do this, we propose a fully differentiable approach for unsupervised feature selection, utilizing the Laplacian score criterion to avoid the selection of nuisance features. We employ an autoencoder architecture to cope with correlated features, trained to reconstruct the data from the subset of selected features. Building on the recently proposed concrete layer that allows controlling for the number of selected features via architectural design, simplifying the optimization process. Experimenting on several real-world datasets, we demonstrate that our proposed approach outperforms similar approaches designed to avoid only correlated or nuisance features, but not both. Several state-of-the-art clustering results are reported. (C) 2022 Elsevier Ltd. All rights reserved.
Solis-Perez, J. E.Hernandez, J. A.Parrales, A.Gomez-Aguilar, J. F....
13页
查看更多>>摘要：This research proposes a novel transfer function based on the hyperbolic tangent and the Khalil conformable exponential function. The non-integer order transfer function offers a suitable neural network configuration because of its ability to adapt. Consequently, this function was introduced into neural network models for three experimental cases: estimating the annular Nusselt number correlation to a helical double-pipe evaporator, the volumetric mass transfer coefficient in an electrochemical reaction, and the thermal efficiency of a solar parabolic trough collector. We found the new transfer function parameters during the training step of the neural networks. Therefore, weights and biases depend on them. We assessed the models applied to the three cases using the determination coefficient, adjusted determination coefficient, and the slope-intercept test. In addition, the MSE for the training set and the whole database were computed to show that there is no overfitting problem. The best-assessed models showed a relationship of 99%, 97%, and 95% with the experimental data for the first, second, and third cases. This novel proposal made reducing the number of neurons in the hidden layer feasible. Therefore, we show a neural network with a conformable transfer function (ANN-CTF) that learns well enough with less available information from the experimental database during its training.
查看更多>>摘要：Two-photon fluorescence microscopy has enabled the three-dimensional (3D) neural imaging of deep cortical regions. While it can capture the detailed neural structures in the x-y image space, the image quality along the depth direction is lower because of lens blur, which often makes it difficult to identify the neural connectivity. To address this problem, we propose a novel approach for restoring the isotropic image volume by estimating and fusing the intersection regions of the images captured from three orthogonal viewpoints using convolutional neural networks (CNNs). Because convolution on 3D images is computationally complex, the proposed method takes the form of cascaded CNN models consisting of rigid transformation, dense registration, and deblurring networks for more efficient processing. In addition, to enable self-supervised learning, we trained the CNN models with simulated synthetic images by considering the distortions of the microscopic imaging process. Through extensive experiments, the proposed method achieved substantial image quality improvements.(c) 2022 Elsevier Ltd. All rights reserved.
Ben-Iwhiwhu, EseogheneDick, JefferyKetz, Nicholas A.Pilly, Praveen K....
10页
查看更多>>摘要：Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic representations in an agent's policy network (obtained via reasoning about task context, model parameter updates, or both). However, obtaining rich dynamic representations for fast adaptation beyond simple benchmark problems is challenging due to the burden placed on the policy network to accommodate different policies. This paper addresses the challenge by introducing neuromodulation as a modular component to augment a standard policy network that regulates neuronal activities in order to produce efficient dynamic representations for task adaptation. The proposed extension to the policy network is evaluated across multiple discrete and continuous control environments of increasing complexity. To prove the generality and benefits of the extension in meta-RL, the neuromodulated network was applied to two state-of-the-art meta-RL algorithms (CAVIA and PEARL). The result demonstrates that meta-RL augmented with neuromodulation produces significantly better result and richer dynamic representations in comparison to the baselines. (C) 2022 The Authors. Published by Elsevier Ltd.
查看更多>>摘要：This paper studies the multistability of delayed recurrent neural networks (DRNNs) with a class of piecewise nonlinear activation functions. The coexistence as well as the stability of multiple equilibrium points (EPs) of DRNNs are proved. With the Brouwer's fixed point theorem as well as the Lagrange mean value theorem, it is obtained that under some conditions, the n-neuron DRNNs with the proposed activation function can have at least 5(n) EPs and 3(n) of them are locally stable. Compared with the DRNNs with sigmoidal activation functions, DRNNs with this kind of activation function can have more total EPs and more locally stable EPs. It implies that when designing DRNNs with the proposed activation function to apply in associative memory, it can have an even larger storage capacity. Furthermore, it is obtained that there exists a relationship between the number of the total EPs/stable EPs and the frequency of the sinusoidal function in the proposed activation function. Last, the above obtained results are extended to a more general case. It is shown that, DRNNs with the extended activation function can have (2k + 1)(n) EPs, (k + 1)(n) of which are locally stable, therein k is closely related to the frequency of the sinusoidal function in the extended activation function. Two simulation examples are given to verify the correctness of the theoretical results. (C) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Reinforcement learning algorithms are typically limited to learning a single solution for a specified task, even though diverse solutions often exist. Recent studies showed that learning a set of diverse solutions is beneficial because diversity enables robust few-shot adaptation. Although existing methods learn diverse solutions by using the mutual information as unsupervised rewards, such an approach often suffers from the bias of the gradient estimator induced by value function approximation. In this study, we propose a novel method that can learn diverse solutions without suffering the bias problem. In our method, a policy conditioned on a continuous or discrete latent variable is trained by directly maximizing the variational lower bound of the mutual information, instead of using the mutual information as unsupervised rewards as in previous studies. Through extensive experiments on robot locomotion tasks, we demonstrate that the proposed method successfully learns an infinite set of diverse solutions by learning continuous latent variables, which is more challenging than learning a finite number of solutions. Subsequently, we show that our method enables more effective few-shot adaptation compared with existing methods. (C) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Artificial neural network has been fully developed in recent years, but as the size of the network grows, the required computing power also grows rapidly. In order to take advantage of the parallel computing of quantum computing to solve the difficulties of large computation in neural network, quantum neural network was proposed. In this paper, based on the pulse coupled neural network (PCNN), quantum pulse coupled neural network (QPCNN) is proposed. In this model, the basic quantum logic gates are utilized to form quantum operation modules, such as quantum full adder, quantum multiplier, and quantum comparator. A quantum image convolution operation applicable to QPCNN is designed employing quantum full adders and neighborhood preparation module. And these modules are employed to complete the operations required for QPCNN. And based on QPCNN, an quantum image segmentation is designed. Meanwhile, the effectiveness of QPCNN is proved by simulation experiments, and the complexity analysis shows that QPCNN has exponential speedup compared with classical PCNN. (C) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Wind power is a new type of green energy. Though it is economical to access and gather such energy, effectively matching the energy with consumers' demand is difficult, because of the fluctuate, intermittent and chaotic nature of wind speed. Hence, multi-step wind speed prediction becomes an important research topic. In this paper, we propose a novel deep learning method, DyanmicNet, for the problem. DynamicNet follows an encoder-decoder framework. To capture the fluctuate, intermittent and chaotic nature of wind speed, it leverages a time-variant structure to build the decoder, which is different from conventional encoder-decoder methods. In addition, a new neural block (ST-GRU-ODE) is developed, which can model the wind speed in a continuous manner by using the neural ordinary differential equation (ODE). To enhance the prediction performance, a multi-step training procedure is also put forward. Comprehensive experiments have been conducted on two real-world datasets, where wind speed is recorded in the form of two orthogonal components namely U-Wind and V-Wind. Each component can be illustrated as wind speed images. Experimental results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art techniques. (C) 2022 Elsevier Ltd. All rights reserved.
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