Search Results for author:
Found 8 papers, 6 papers with code
Harnessing Orthogonality to Train Low-Rank Neural Networks
This study explores the learning dynamics of neural networks by analyzing the singular value decomposition (SVD) of their weights throughout training.
Thermal Bridges on Building Rooftops
Thermal Bridges on Building Rooftops (TBBR) is a multi-channel remote sensing dataset.
Feed-Forward Optimization With Delayed Feedback for Neural Networks
Backpropagation has long been criticized for being biologically implausible, relying on concepts that are not viable in natural learning processes.
Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations
We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search.
Learning Tree Structures from Leaves For Particle Decay Reconstruction
In the case of high-energy particle physics, a particle decay forms a hierarchical tree structure of which only the final products can be observed experimentally, and the large combinatorial space of possible trees makes an analytic solution intractable.
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
Furthermore, we present a method for training DNNs for denoising HSIs which are not spatially related to the training dataset, i. e., training on ground-level HSIs for denoising HSIs with other perspectives including airborne, drone-borne, and space-borne.
Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)
With increasing data and model complexities, the time required to train neural networks has become prohibitively large.
HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics
With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis.
