Search Results for author: Jacob Hinkle

Found 8 papers, 1 papers with code

Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction

no code implementations12 May 2022 Xiao Wang, Aristeidis Tsaris, Debangshu Mukherjee, Mohamed Wahib, Peng Chen, Mark Oxley, Olga Ovchinnikova, Jacob Hinkle

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

no code implementations22 Mar 2021 Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan

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.

Autonomous Driving Decision Making +1

Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation

no code implementations19 Dec 2020 Abhishek K Dubey, Michael T Young, Christopher Stanley, Dalton Lunga, Jacob Hinkle

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.

Anomaly Detection Domain Adaptation

Model Reduction of Shallow CNN Model for Reliable Deployment of Information Extraction from Medical Reports

no code implementations31 Jul 2020 Abhishek K Dubey, Alina Peluso, Jacob Hinkle, Devanshu Agarawal, Zilong Tan

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

no code implementations6 Mar 2020 Theodore Papamarkou, Hayley Guy, Bryce Kroencke, Jordan Miller, Preston Robinette, Daniel Schultz, Jacob Hinkle, Laura Pullum, Catherine Schuman, Jeremy Renshaw, Stylianos Chatzidakis

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

no code implementations3 Jan 2020 Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle

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.

Gaussian Processes

Challenges in Markov chain Monte Carlo for Bayesian neural networks

1 code implementation15 Oct 2019 Theodore Papamarkou, Jacob Hinkle, M. Todd Young, David Womble

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.

Bayesian Inference

Learning nonlinear level sets for dimensionality reduction in function approximation

no code implementations NeurIPS 2019 Guannan Zhang, Jiaxin Zhang, Jacob Hinkle

We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data.

Functional Analysis

Cannot find the paper you are looking for? You can Submit a new open access paper.