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Showing 1 to 5 of 5 for “"convolutional neural nets"”.

  1. Applied Plankton Image Classification for Imaging FlowCytobot Data

    … images of plankton gathered by the IFCB - Convolutional Neural Nets (CNNs), Vision Transformers (ViT), and self-supervised learning (MAE). The benefits and downsides of each model are analyzed and discussed for future IFCB operators to process their data using the methods that best align …

    mit Repository record for Applied Plankton Image Classification for Imaging FlowCytobot Data (opens in a new tab)

  2. Power efficient machine learning-based hardware architectures for biomedical applications

    … binarized digital hardware technique. Neural network models, such as feedforward, convolutional neural nets, residual networks, and other popular machine learning and deep neural networks, are selected to benchmark the proposed model architecture. Various deep compression learning …

    missouri Repository record for Power efficient machine learning-based hardware architectures for biomedical applications (opens in a new tab)

  3. Improving utilization, granularity, and interpretability in visual representation learning

    … latent component analysis (PLCA) into deep convolutional neural nets (CNNs). Hence we call our method Deep PLCA. Intuitively, PLCA decomposes image data into local structures (kernels), and their spatial locations (latent components). Compared to PLCA, Deep PLCA achieves the same …

    uiuc Repository record for Improving utilization, granularity, and interpretability in visual representation learning (opens in a new tab)

  4. Universal approximation of input-output maps and dynamical systems by neural network architectures

    It is well known that feedforward neural networks can approximate any continuous function supported on a finite-dimensional compact set to arbitrary accuracy. However, many engineering applications require modeling infinite-dimensional functions, such as sequence-to-sequence transformations or …

    uiuc Repository record for Universal approximation of input-output maps and dynamical systems by neural network architectures (opens in a new tab)