1 citations · 2 across the 3 of their papers we have counts for
5 papers
Improved Imaging by Invex Regularizers with Global Optima Guarantees
Samuel Pinilla, Tingting Mu, Neil Bourne +1
Image reconstruction enhanced by regularizers, e.g., to enforce sparsity, low rank or smoothness priors on images, has many successful applications in vision tasks such as computer…
Disentangling Autoencoders (DAE)
Jaehoon Cha, Jeyan Thiyagalingam
Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of…
Scientific Machine Learning Benchmarks
Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox +1
The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datase…
Benchmarking and Scalability of Machine Learning Methods for Photometric Redshift Estimation
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam +2
Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift es…
Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data
Keith T. Butler, Manh Duc Le, Jeyarajan Thiyagalingam +1
Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' pe…