I’m a PhD student focusing on the application of machine learning to electron microscopy. This mainly means GPU-accelerated neural network training with TensorFlow. So far, I have written papers on
- Improving electron micrograph signal-to-noise, especially in low-dose micrographs: https://arxiv.org/abs/1807.11234. This is achieved using a large neural network based on Xception.
- Electron micrograph restoration and compression: https://arxiv.org/abs/1808.09916. This features 14 autoencoders, 15 kernels and 14 multilayer perceptrons. These can all be used for image restoration and the autoencoders have been trained for compression ratios up to 64:1.
- Neural networks that play with each other to reduce scanning transmission electron micrograph beam exposure and scan time: https://arxiv.org/abs/1905.13667. We are actively developing an experimental system.
- Adaptive learning rate clipping (ALRC) to limit backpropagated losses: https://arxiv.org/abs/1906.09060. ALRC complements existing learning algorithms to improve training stability, accelerate convergence and lower errors.
If you want to know more about what I’m doing, most of my work is on GitHub: https://github.com/Jeffrey-Ede
Datasets have been moved here.