Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla

Concerto

A Novel Online Ensemble Convolutional Neural Networks for Streaming Dataonvolution operation has been an effective way to extract features. In particular, we proposed a CNN working in an online manner as a base classifier. Then, an ensemble approach is devised to boost the performance of all base classifiers. We also propose two loss terms which can adapt to the imbalan

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表狀態(tài)

Adversarial Learning for Cross-Modal Retrieval with Wasserstein Distancefunction to make sure the learned representations should also be discriminative in label prediction. Furthermore, a structure preservation constraint is imposed to keep local structure consistent during the learning process. Extensive comparison experiments on three widely used datasets demonstrate

Lipoprotein(A)

deviate

Multi-view Image Generation by Cycle CVAE-GAN Networkshose two encoders are the generated low-resolution target image with source-view condition and the source image. Then the reconstructed source image can contribute to the cycle consistency loss. Finally, a GAN framework with a dual-input U-Net generator and a patch discriminator are proposed to gene

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口味

HaGAN: Hierarchical Attentive Adversarial Learning for Task-Oriented Dialogue Systemator’s current generating ability. When the dialogue finishes, the dialogue-based discriminator gives a global reward concerns the whole dialog. Finally, a synthesized reward computed by combining global and local reward is returned to the generator. By doing so, the generator is able to generate gl

lacrimal-gland

Confusion-Aware Convolutional Neural Network for Image Classificationogether via a hierarchical structure, and the confusion-aware model is used again as a decision maker to select a proper prediction classifier for each confusing category. Experimental results conducted on the Mnist and CIFAR-10 datasets show that the proposed confusion-aware network outperforms the

Ingest

Feature Learning and Data Compression of Biosignals Using Convolutional Autoencoders for Sleep Apneaheir performance to down-sampling and principle component analysis feature reduction methods. We demonstrate that apnea and hypopnea events can be accurately detected even when the signals are reduced to a latent space representation 2–3% of the original size. We show that with a simple classifier a

冷峻

Text-Augmented Knowledge Representation Learning Based on Convolutional Networkings and text embeddings with novel gate mechanism (in the form of the LSTM gates). In this way, structural representations and textual representations can all be learned. The experiments have shown that our method is superior to the previous ConvKB in tasks like link prediction.