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Found 8 papers, 1 papers with code
Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)
We consider the controllability of large-scale linear networked dynamical systems when complete knowledge of network structure is unavailable and knowledge is limited to coarse summaries.
Compressing gradients by exploiting temporal correlation in momentum-SGD
In this paper we design and analyze compression methods that exploit temporal correlation in systems both with and without error-feedback.
Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope
We leverage epistemic and aleatoric uncertainties in conjunction with probabilistic generative modeling to identify candidate vent adjustments that are in-distribution (ID); for the optimal configuration for each ID sample, we predict the reduction in required observing time to achieve a fixed SNR.
Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model
Our objective is to develop conditions on the graph structure, the quantity, and properties of measurements, under which we can recover the community organization in this coarse graph.
Node-Centric Graph Learning from Data for Brain State Identification
This is particularly true when the number of nodes is high or there are temporal changes in the network.
A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals
Such a model maps signals onto vertices of a graph and the time-space dependencies among signals are modeled by the edge weights.
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization
Anytime Minibatch prevents stragglers from holding up the system without wasting the work that stragglers can complete.
Efficient learning of neighbor representations for boundary trees and forests
In this paper, we introduce Differentiable Boundary Sets, an algorithm that overcomes the computational issues of the differentiable boundary tree scheme and also improves its classification accuracy and data representability.
