A good dataset can provide rich and diverse samples for the deep learning model, so that the model can learn more generalized features and thereby improve its performance. In this study, we use the same dataset as IF-AIP36, which collect the datasets from iAIPs25and AntiInflam17, resulting ...
Regularized Autoencoder:They use a loss function that encourages the model to have other properties besides the ability to copy its input to its output. In practice, we usually find two types of regularized autoencoder: the sparse autoencoder and the denoising autoencoder. Sparse Autoencoder:Spars...
In the realm of artificial intelligence,autoencodershave emerged as a transformative force. This comprehensive guide aims to provide an in-depth exploration ofautoencoders, from its origins to real-world applications and its significance in the AI domain. By uncovering the workings, examples, pros ...
Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS...
A good imputation strategy should improve the separability of various cell-type subpopulations. To assess this, We reduce the gene expression to two dimensions by applying Principal Component analysis on it, and further plotting the cell transcriptomes in 2D space, coloring each cell by its ...
a“domain gap” between training and deployment because of images with missing chunks. Contribution We propose the split-brain autoencoder, which is composed of concatenated cross-channel encoders, trained usingraw dataas its own supervisory signal. ...
The X-Ray is another attractive method because of its flexibility, low cost, and comparatively quicker approach [8,9]. But the characteristics of the disease and its pulmonary consolidations at various stages are not clearly visible in the X-ray images, since they are low-resolution by nature...
All results are for the PointMAE backbone trained on clean train set and adapted to the OOD test set with a batch-size of 1 (copied 48 times through random masking). Source-Only denotes its performance on the corrupted test data without any adaptation. Highest Accura...
cell-line and other in vitro models have been extensively applied to screen drug candidates. Unfortunately, the activity of a compound in vitro is poorly correlated with its efficacy in humans. This discrepancy is responsible for the high cost and low success rate of drug discovery. Even for dr...
[1]. This progress has consolidated the position of hyperspectral technology as a fundamental tool in various sectors. In particular, its relevance in oil spill monitoring stands out, where its ability to analyse and detect subtleties in spectral characteristics allows for more accurate and efficient...