Artificial intelligence is accelerating material discovery and design by automating analysis, guiding experiments, and enabling predictive modeling across spectroscopy, microscopy, and synthesis.

Recent encoder-decoder architectures (Lomtev et al.) preserve detailed information through skip connections but remain constrained by standard convolutions’ limited receptive fields for multi-scale ...

Next-generation U-Net Encoder: Decoder for accurate, automated CTC detection from images of peripheral blood nucleated cells stained with EPCAM and DAPI.. If you have the appropriate software ...

The encoder and decoder are composed of convolutional layer chain and deconvolution layer chain, respectively. The proposed method consists of three main modifications. First, a selection kernel (SK) ...

Document image binarization is one of the critical initial steps for document analysis and understanding. Previous work mostly focused on exploiting hand-crafted features to build statistical models ...

An Encoder-decoder architecture in machine learning efficiently translates one sequence data form to another.

The overall structure of the encoder is designed to achieve a perfect balance of encoding performance and parameters. We design a decoder with a feature pyramid structure, spatial attention, and ...

The maximum likelihood detection of a digital stream is possible by Viterbi algorithm. In this paper, we present a Convolutional encoder and Viterbi decoder with a constraint length of 7 and code rate ...

Add a description, image, and links to the convolutional-encoder-decoder topic page so that developers can more easily learn about it ...