Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction...
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ISBN:
(纸本)9781611978032
Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled variational autoencoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE.
Inverse design is an efficient and powerful design tool in the aircraft industry, however, most of the methods require physically meaningful pressure distributions as an input which deeply relies on designer expertise...
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Inverse design is an efficient and powerful design tool in the aircraft industry, however, most of the methods require physically meaningful pressure distributions as an input which deeply relies on designer expertise. In this paper, it was proposed to reduce the two-dimensional coordinate value data and pressure distribution data of the airfoil through the variational autoencoder. The model maps high-dimensional data to low-dimensional space, and extracted the low-dimensional manifold structure of high-dimensional data. Test cases of a low-speed airfoil and a transonic airfoil were used for pressure distribution prediction. The result shows that the VAE can achieve high accuracy for pressure distribution prediction. A framework for inverse design of airfoils was also established, and the objective function was the difference between the target pressure and the design pressure. Using a global optimization algorithm to optimization in the low-dimensional space, and a physically meaningful aerodynamic shape and pressure distribution was obtained by the trained model. The VAE model acted like a surrogate model, and the hidden space dimension is low, so the global optimal solution can be efficiently found when the number of populations and iteration steps are small. In the method, the target pressure distribution was defined without a strong dependence on the designer's experience, achieving a rapid inverse design at the minute level.
Ultrasound imaging has become a preferred medical diagnostics tool for many applications due to its cost-effectiveness, non-ionizing nature, and real-time capabilities. There has been a significant progress in the dev...
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ISBN:
(纸本)9798350317107;9798350317114
Ultrasound imaging has become a preferred medical diagnostics tool for many applications due to its cost-effectiveness, non-ionizing nature, and real-time capabilities. There has been a significant progress in the development of new ultrasound probes and systems, particularly portable and wearable devices, incorporating new transducer technologies, sophisticated electronics integration, artificial intelligence and advanced beamforming strategies. Wearable ultrasound systems, equipped with wireless data transfer interfaces, offer unique advantages for continuous signal monitoring of the patients for their critical conditions both in and out-of-hospital settings. Many challenges specifically in data rate reduction for wireless real-time systems needs to be explored. To address this issue, in this paper, we present a vector quantized variational autoencoder model to effectively compress ultrasound RF signals without compromising image quality. We tested and evaluated the performance of the model on real ultrasound datasets. The experimental results demonstrate 92% of data reduction enabling achievable real-time imaging speeds over wireless channels.
Ultrasound (US) is widely employed in medical imaging, but its use in the human thorax is constrained by high attenuation. Recently, researchers have addressed this limitation by utilizing low-frequency US (10 kHz to ...
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ISBN:
(纸本)9798350371918;9798350371901
Ultrasound (US) is widely employed in medical imaging, but its use in the human thorax is constrained by high attenuation. Recently, researchers have addressed this limitation by utilizing low-frequency US (10 kHz to 750 kHz), which can penetrate the thorax. Studies indicate that low-frequency US can monitor the respiratory system through attenuation factor differential imaging, reconstructing variations in the thorax attenuation factor based on changes in transmitted US energy during respiration. In this paper, we propose a novel inversion method using a neural network structure, the variational autoencoder (VAE), to integrate prior knowledge of thorax structure. The VAE is trained to compress high-dimensional data into a low-dimensional code and subsequently reconstruct it. During inversion, the latent code of the domain of interest (DOI) is first reconstructed, followed by decoding to obtain the high-dimensional attenuation distribution, effectively incorporating structural prior information. Numerical simulations with both training and test set models demonstrate the superiority of the proposed method.
Knowledge tracking (KT) is a task that predicting the degree of students' knowledge mastery through their learning interaction records. Although existing works improve predictive capability with well-designed neur...
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ISBN:
(纸本)9789819772438;9789819772445
Knowledge tracking (KT) is a task that predicting the degree of students' knowledge mastery through their learning interaction records. Although existing works improve predictive capability with well-designed neural network models or hypothetical learning mechanisms, the predictive performance is compromised in the scenarios of quantity limited interaction data. In this paper, we utilize variational autoencoder (VAE) and pre-trained network to generate question answer sequence data pairs related to the original interaction data, which can improve the performance of the model when added to the training set even in the case of data scarcity. Specifically, the steps of the data augmentation method for KT we proposed are as follows: 1) Question sequence generation. Generate latent question sequences that are similar to the real interaction question sequences from the pre-designed VAE model. 2) Answer sequence generation. Put the generated data into the pre-trained KT model to get reliable answer label sequences that correspond to latent question sequences. 3) Samples generation and training. Combine the two types of generated sequences as new samples for KT task training. We apply the data augmentation method on four classic datasets and demonstrate its effectiveness by reaching the state-of-the-art performance with an average AUC index improvement of 2.41%. We also verify the method on artificially random extracted data, and with only 20% of the data, it even achieves similar results compared with other methods using 100% of the data.
In recent years, there has been a growing interest in probabilistic forecasting methods that offer more comprehensive insights by considering prediction uncertainties rather than point estimates. This paper introduces...
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ISBN:
(纸本)9798400704369
In recent years, there has been a growing interest in probabilistic forecasting methods that offer more comprehensive insights by considering prediction uncertainties rather than point estimates. This paper introduces a novel variational autoencoder learning framework for multivariate distributional forecasting. Our approach employs distributional learning to directly estimate the cumulative distribution function of future time series conditional distributions using the continuous ranked probability score. By incorporating a temporal structure within the latent space and utilizing versatile quantile models, such as the generalized lambda distribution, we enable distributional forecasting by generating synthetic time series data for future time points. To assess the effectiveness of our method, we conduct experiments using a multivariate dataset of real cryptocurrency prices, demonstrating its superiority in forecasting high-volatility scenarios.
Due to its ability to reveal tissue heterogeneity, spatial analytic transcriptomic data has been used to decipher the spatial domain of complex diseases for precise treatment. At present, the problems of high dimensio...
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ISBN:
(纸本)9798350390780;9798350379228
Due to its ability to reveal tissue heterogeneity, spatial analytic transcriptomic data has been used to decipher the spatial domain of complex diseases for precise treatment. At present, the problems of high dimensional, sparse and noisy data in the application process are awaiting to be ***, it has not fully leveraged the intercellular molecular interactions, which affect the accuracy of spatial domain identification. In order to solve this problem, the stMVC model is improved by integrating the single sample network method and variational autoencoder, and the spatial domain(SDI-VASSN) is extracted from the transcriptome data of multimodal spatial decomposition. Specifically, the model uses multimodal data of the human dorsolateral prefrontal cortex obtained through 10X Genomics Visium technology. Firstly, from the samples, we used cell specific molecular interaction network (CSN) to calculate the gene interaction network of each cell and extract key genes;Then we encode key genes by one-hot coding;Finally, we use variational autoencoder (VAE) instead of autoencoder (AE) to maximize the probability of the input data to learn the probability distribution of the data, thus input the resulting data into the stMVC model for identifying the spatial domain.
Time series forecasting based on decomposition method usually decomposes a complex time series into some simple components, such as long-term and seasonal trends, which are more easy to be predicted. Though long-term ...
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ISBN:
(纸本)9798350359329;9798350359312
Time series forecasting based on decomposition method usually decomposes a complex time series into some simple components, such as long-term and seasonal trends, which are more easy to be predicted. Though long-term and seasonal trends are vital in time series foreasting, it may be insufficient for those not having such obvious characters. This paper proposes a time series forecasting model named SD-VAE based on structured decomposition and variational autoencoder(VAE). The structured decomposition module decomposes a time series into long-term component, seasonal component, short-term component and co-evolving component, and the VAE module learns the representation of them, through which the future value of each decomposed component are predicted and then fused by a neural network. To get a better decoupled representation of each decomposed component, mutual information constraints are added in the latent space of VAE. Extensive experiments prove that our model performs the best in 3 out of 4 public datasets, with the other one ranking second, against the most representation learning and end-to-end forecasting models.
Recently, with increasing interest in pet healthcare, the demand for computer-aided diagnosis (CAD) systems in veterinary medicine has increased. The development of veterinary CAD has stagnated due to a lack of suffic...
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ISBN:
(纸本)9798350354102;9798350354096
Recently, with increasing interest in pet healthcare, the demand for computer-aided diagnosis (CAD) systems in veterinary medicine has increased. The development of veterinary CAD has stagnated due to a lack of sufficient radiology data. To overcome the challenge, we propose a generative active learning framework based on a variational autoencoder. This approach aims to alleviate the scarcity of reliable data for CAD systems in veterinary medicine. This study utilizes datasets comprising cardiomegaly radiographic image data and chronic kidney disease ultrasound image data. After removing annotations and standardizing images, we employed a framework for data augmentation, which consists of a data generation phase and a query phase for filtering the generated data. The experimental results revealed that as the data generated through this framework was added to the training data of the generative model, the frechet inception distance decreased from 84.14 to 50.75 in the radiographic image and from 127.98 to 35.16 in an ultrasound image. Subsequently, when the generated data were incorporated into the training of the classification model, the true negative of the confusion matrix also improved from 0.16 to 0.66 on the radiograph and from 0.44 to 0.64 on the ultrasound image. The proposed framework has the potential to address the challenges of data scarcity in medical CAD, contributing to its advancement.
In widely used neural network-based collaborative filtering models, users' history logs are encoded into latent embeddings that represent the users' preferences. In this setting, the models are capable of mapp...
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ISBN:
(纸本)9783031719745;9783031719752
In widely used neural network-based collaborative filtering models, users' history logs are encoded into latent embeddings that represent the users' preferences. In this setting, the models are capable of mapping users' protected attributes (e.g., gender or ethnicity) from these user embeddings even without explicit access to them, resulting in models that may treat specific demographic user groups unfairly and raise privacy issues. While prior work has approached the removal of a single protected attribute of a user at a time, multiple attributes might come into play in real-world scenarios. In the work at hand, we present ADVXMULTVAE which aims to unlearn multiple protected attributes (exemplified by gender and age) simultaneously to improve fairness across demographic user groups. For this purpose, we couple a variational autoencoder (VAE) architecture with adversarial training (ADVMULTVAE) to support simultaneous removal of the users' protected attributes with continuous and/or categorical values. Our experiments on two datasets, LFM-2B-100K and ML-1M, from the music and movie domains, respectively, show that our approach can yield better results than its singular removal counterparts (based on ADVMULTVAE) in effectively mitigating demographic biases whilst improving the anonymity of latent embeddings.
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