Mathematical expressions were generated, evaluated and used to train neural network models based on the transformer architecture.
Holographic wave-shaping has found numerous applications across the physical sciences, especially since the development of digital spatial-light modulators (SLMs).
Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) image classifier, as demonstrated in prior work.
Many transformations in deep learning architectures are sparsely connected.
Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks.
Reconfigurable photonic mesh networks of tunable beamsplitter nodes can linearly transform $N$-dimensional vectors representing input modal amplitudes of light for applications such as energy-efficient machine learning hardware, quantum information processing, and mode demultiplexing.
We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide.