5 papers
Generative Kaleidoscopic Networks
Harsh Shrivastava
We discovered that the neural networks, especially the deep ReLU networks, demonstrate an `over-generalization' phenomenon. That is, the output values for the inputs that were not…
Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Federated Learning (FL) addresses the need to create models based on proprietary data in such a way that multiple clients retain exclusive control over their data, while all benefi…
Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska, Harsh Shrivastava
Conditional Independence (CI) graph is a special type of a Probabilistic Graphical Model (PGM) where the feature connections are modeled using an undirected graph and the edge weig…
Are uGLAD? Time will tell!
Shima Imani, Harsh Shrivastava
We frequently encounter multiple series that are temporally correlated in our surroundings, such as EEG data to examine alterations in brain activity or sensors to monitor body mov…
Neural Graph Revealers
Harsh Shrivastava, Urszula Chajewska
Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries. This limits their adopt…