128 citations · 215 across the 8 of their papers we have counts for
4 papers · 1 filter
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
Meet P. Vadera, Adam D. Cobb, Brian Jalaian +1
Bayesian methods hold significant promise for improving the uncertainty quantification ability and robustness of deep neural network models. Recent research has seen the investigat…
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Yushun Dong, Ninghao Liu, Brian Jalaian +1
Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed networks in various web-based applications such as social recommendation and web search. Nevert…
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo
Vyacheslav Kungurtsev, Adam Cobb, Tara Javidi +1
Federated learning performed by a decentralized networks of agents is becoming increasingly important with the prevalence of embedded software on autonomous devices. Bayesian appro…
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
Yushun Dong, Kaize Ding, Brian Jalaian +2
Graph Neural Networks have recently become a prevailing paradigm for various high-impact graph analytical problems. Existing efforts can be mainly categorized as spectral-based and…