| 1. |
- Ding, Yijie, et al.
(författare)
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Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization
- 2022
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 156, s. 170-178
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Tidskriftsartikel (refereegranskat)abstract
- Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs is a necessary way to study ncRNA. Traditional biochemical methods are time-consuming and labor-intensive, and computational-based methods can help detect the location of ncRNAs on a large scale. However, many models did not consider the correlation information among multiple subcellular localizations of ncRNAs. This study proposes a radial basis function neural network based on shared subspace learning (RBFNN-SSL), which extract shared structures in multi-labels. To evaluate performance, our classifier is tested on three ncRNA datasets. Our model achieves better performance in experimental results. © 2022 The Author(s)
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| 2. |
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| 3. |
- Guo, Xiaoyi, et al.
(författare)
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Sequence homology score-based deep fuzzy network for identifying therapeutic peptides
- 2024
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Ingår i: Neural Networks. - Kidlington : Elsevier. - 0893-6080 .- 1879-2782. ; 178
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Tidskriftsartikel (refereegranskat)abstract
- The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923). © 2024 The Authors
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| 4. |
- Han, Ridong, et al.
(författare)
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Document-level Relation Extraction with Relation Correlations
- 2024
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 171, s. 14-24
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Tidskriftsartikel (refereegranskat)abstract
- Document-level relation extraction faces two often overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into the document-level relation extraction task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of long-tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular datasets (i.e., DocRED and DWIE) are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. The data and code are released at https://github.com/RidongHan/DocRE-Co-Occur. © 2023 Elsevier Ltd
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| 5. |
- Karami, Saeed, et al.
(författare)
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Unsupervised feature selection based on variance–covariance subspace distance
- 2023
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 166, s. 188-203
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Tidskriftsartikel (refereegranskat)abstract
- Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called “Variance–Covariance subspace distance”. The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance–Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance–Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS. © 2023 The Author(s)
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| 6. |
- Rongala, Udaya B., et al.
(författare)
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Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions
- 2020
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Ingår i: Neural Networks. - : Elsevier BV. - 0893-6080. ; 123, s. 273-287
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Tidskriftsartikel (refereegranskat)abstract
- We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions. Tactile sensor data acquired with five textures under five sensing conditions were used for a synaptic learning process, to tune the synaptic weights between tactile afferents and cuneate neurons. Using post-learning synaptic weights, we evaluated the individual and population cuneate neuron responses by decoding across 10 stimuli, under varying sensing conditions. This resulted in a high decoding performance. We further validated the decoding performance across stimuli, irrespective of sensing velocities using a set of 25 cuneate neuron responses. This resulted in a median decoding performance of 96% across the set of cuneate neurons. Being able to learn and perform generalized discrimination across tactile stimuli, makes this functional spiking tactile system effective and suitable for further robotic applications.
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| 7. |
- Saeed, Uzair, et al.
(författare)
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One-shot many-to-many facial reenactment using Bi-Layer Graph Convolutional Networks
- 2022
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 156, s. 193-204
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Tidskriftsartikel (refereegranskat)abstract
- Facial reenactment is aimed at animating a source face image into a new place using a driving facial picture. In a few shot scenarios, the present strategies are designed with one or more identities or identity-sustained suffering protection challenges. These current solutions are either developed with one or more identities in mind, or face identity protection issues in one or more shot situations. Multiple pictures from the same entity have been used in previous research to model facial reenactment. In contrast, this paper presents a novel model of one-shot many-to-many facial reenactments that uses only one facial image of a face. The proposed model produces a face that represents the objective representation of the same source identity. The proposed technique can simulate motion from a single image by decomposing an object into two layers. Using bi-layer with Convolutional Neural Network (CNN), we named our model Bi-Layer Graph Convolutional Layers (BGCLN) which utilized to create the latent vector’s optical flow representation. This yields the precise structure and shape of the optical stream. Comprehensive studies suggest that our technique can produce high-quality results and outperform most recent techniques in both qualitative and quantitative data comparisons. Our proposed system can perform facial reenactment at 15 fps, which is approximately real time. Our code is publicly available at https://github.com/usaeed786/BGCLN
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| 8. |
- Sheikholharam Mashhadi, Peyman, 1982-, et al.
(författare)
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Parallel orthogonal deep neural network
- 2021
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Ingår i: Neural Networks. - Oxford : Elsevier BV. - 0893-6080 .- 1879-2782. ; 140, s. 167-183
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Tidskriftsartikel (refereegranskat)abstract
- Ensemble learning methods combine multiple models to improve performance by exploiting their diversity. The success of these approaches relies heavily on the dissimilarity of the base models forming the ensemble. This diversity can be achieved in many ways, with well-known examples including bagging and boosting.It is the diversity of the models within an ensemble that allows the ensemble to correct the errors made by its members, and consequently leads to higher classification or regression performance. A mistake made by a base model can only be rectified if other members behave differently on that particular instance, and provide the aggregator with enough information to make an informed decision. On the contrary, lack of diversity not only lowers model performance, but also wastes computational resources. Nevertheless, in the current state of the art ensemble approaches, there is no guarantee on the level of diversity achieved, and no mechanism ensuring that each member will learn a different decision boundary from the others.In this paper, we propose a parallel orthogonal deep learning architecture in which diversity is enforced by design, through imposing an orthogonality constraint. Multiple deep neural networks are created, parallel to each other. At each parallel layer, the outputs of different base models are subject to Gram–Schmidt orthogonalization. We demonstrate that this approach leads to a high level of diversity from two perspectives. First, the models make different errors on different parts of feature space, and second, they exhibit different levels of uncertainty in their decisions. Experimental results confirm the benefits of the proposed method, compared to standard deep learning models and well-known ensemble methods, in terms of diversity and, as a result, classification performance. © 2021 The Author(s). Published by Elsevier Ltd.
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| 9. |
- Tian, Songsong, et al.
(författare)
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A survey on few-shot class-incremental learning
- 2024
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 169, s. 307-324
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Forskningsöversikt (refereegranskat)abstract
- Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective. © 2023 The Author(s)
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| 10. |
- Vasco, M., et al.
(författare)
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Leveraging hierarchy in multimodal generative models for effective cross-modality inference
- 2022
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Ingår i: Neural Networks. - : Elsevier BV. - 0893-6080 .- 1879-2782. ; 146, s. 238-255
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Tidskriftsartikel (refereegranskat)abstract
- This work addresses the problem of cross-modality inference (CMI), i.e., inferring missing data of unavailable perceptual modalities (e.g., sound) using data from available perceptual modalities (e.g., image). We overview single-modality variational autoencoder methods and discuss three problems of computational cross-modality inference, arising from recent developments in multimodal generative models. Inspired by neural mechanisms of human recognition, we contribute the NEXUS model, a novel hierarchical generative model that can learn a multimodal representation of an arbitrary number of modalities in an unsupervised way. By exploiting hierarchical representation levels, NEXUS is able to generate high-quality, coherent data of missing modalities given any subset of available modalities. To evaluate CMI in a natural scenario with a high number of modalities, we contribute the “Multimodal Handwritten Digit” (MHD) dataset, a novel benchmark dataset that combines image, motion, sound and label information from digit handwriting. We access the key role of hierarchy in enabling high-quality samples during cross-modality inference and discuss how a novel training scheme enables NEXUS to learn a multimodal representation robust to missing modalities at test time. Our results show that NEXUS outperforms current state-of-the-art multimodal generative models in regards to their cross-modality inference capabilities.
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| 11. |
- Wu, Hongjie, et al.
(författare)
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AttentionMGT-DTA : A multi-modal drug-target affinity prediction using graph transformer and attention mechanism
- 2024
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 169, s. 623-636
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Tidskriftsartikel (refereegranskat)abstract
- The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction. However, one challenge for deep learning-based models is appropriate and accurate representations of drugs and targets, especially the lack of effective exploration of target representations. Another challenge is how to comprehensively capture the interaction information between different instances, which is also important for predicting DTA. In this study, we propose AttentionMGT-DTA, a multi-modal attention-based model for DTA prediction. AttentionMGT-DTA represents drugs and targets by a molecular graph and binding pocket graph, respectively. Two attention mechanisms are adopted to integrate and interact information between different protein modalities and drug-target pairs. The experimental results showed that our proposed model outperformed state-of-the-art baselines on two benchmark datasets. In addition, AttentionMGT-DTA also had high interpretability by modeling the interaction strength between drug atoms and protein residues. Our code is available at https://github.com/JK-Liu7/AttentionMGT-DTA. © 2023 The Author(s)
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