In this figure, we have used circles to also denote the inputs to the network. The circles labeled "+1" are calledbias units, and correspond to the intercept term. The leftmost layer of the network is called theinput layer, and the rightmost layer theoutput layer(which, in this example,...
In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from...
Last but not least, QuickSelection computational efficiency makes it have the minimum energy consumption among the autoencoder-based feature selection methods considered. Fig. 1 A high-level overview of the proposed method, “QuickSelection”. a At epoch 0, connections are randomly initialized. b ...
Here, we propose Physics-Informed Masked Autoencoder (PI-MAE), a hardware implementation of Masked Autoencoder (MAE)17,18,19. MAE is a self-supervised scalable computer vision model thatdigitally masksinput images and reconstructs their masked pixels, as masked learning yields accelerated training ...
a, Detailed LoopDenoise convolutional autoencoder model architecture showing five convolution layers, two in the encoding path using eight 13 × 13 filters, two transpose convolution layers in the decoding path using eight 2 × 2 filters and one final convolution layer using a single 13...
On the other hand, the convolutional neural network (CNN) and autoencoder (AE) prefer non-sequential data types, such as image input [14]. The algorithms attempt to distinguish between normal and anomalous behavior by establishing a decision boundary, such as with the support vector machine (...
A novel Transformer similar to masked autoencoder (MAE) [3] that yields complete 3D scene representation. • VoxFormer sets a new state-of-the-art in camera-based SSC on SemanticKITTI [5], as shown in Fig. 1 (b). VoxFormer consists of class-agnostic query proposal (stage-1) and ...
[33]. To access an approximation of the Pareto-optimal subspace, a restricted boltzmann machine and a denoising autoencoder are respectively adopted to acquire the sparse distribution and the compact representation, where retained dimensions of decision variables are determined by the hidden layer size...
deep sparse autoencoder; support vector data description; multilayer bidirectional long–short–term memory network; remaining useful life; gear pump1. Introduction As the complexity of mechanical equipment continues to increase, and the equipment’s maintenance cost gradually rises, researchers have to ...
Keywords: electronic nose; self-taught learning; sparse autoencoder; wound infection 1. Introduction Electronic nose (E-nose), a device composed of a sensor array and an artificial intelligence algorithm, has been successfully used in many fields. It is able to deal with a multitude of problems...