84 citations · 304 across the 24 of their papers we have counts for
9 papers · 1 filter
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Consider a set of data points in the Euclidean space . This set is called dataset in machine learning and data science. Manifold hypothesis states that the datase…
Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees
Juan Carrillo, Daniel Garijo, Mark Crowley +3
Climate science is critical for understanding both the causes and consequences of changes in global temperatures and has become imperative for decisive policy-making. However, clim…
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Colin Bellinger, Rory Coles, Mark Crowley +1
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design. Recent work includes, for example, the design of…
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei +3
Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese conc…
Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
After the tremendous development of neural networks trained by backpropagation, it is a good time to develop other algorithms for training neural networks to gain more insights int…
Anomaly Detection and Prototype Selection Using Polyhedron Curvature
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature. Using the nearest neighbors for…