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Found 8 papers, 1 papers with code
Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction
In this paper, we propose a novel image gradient decomposition method that significantly reduces the memory footprint for ptychographic reconstruction by tessellating image gradients and diffraction measurements into tiles.
Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis.
Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation
These pre-trained DL models' ability to generalize in clinical settings is poor because of the changes in data distributions between publicly available and privately held radiographs.
Model Reduction of Shallow CNN Model for Reliable Deployment of Information Extraction from Medical Reports
Shallow Convolution Neural Network (CNN) is a time-tested tool for the information extraction from cancer pathology reports.
Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks
The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister.
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
There has recently been much work on the "wide limit" of neural networks, where Bayesian neural networks (BNNs) are shown to converge to a Gaussian process (GP) as all hidden layers are sent to infinite width.
Challenges in Markov chain Monte Carlo for Bayesian neural networks
Nevertheless, this paper shows that a non-converged Markov chain, generated via MCMC sampling from the parameter space of a neural network, can yield via Bayesian marginalization a valuable posterior predictive distribution of the output of the neural network.
Learning nonlinear level sets for dimensionality reduction in function approximation
We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data.
Functional Analysis
