10 citations · 10 across the 3 of their papers we have counts for
5 papers
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…
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…
Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization
Subhodip Biswas, Adam D Cobb, Andreea Sistrunk +2
In this paper, we propose a surrogate-assisted evolutionary algorithm (EA) for hyperparameter optimization of machine learning (ML) models. The proposed STEADE model initially esti…
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
Adam D. Cobb, Brian Jalaian
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) approach that exhibits favourable exploration properties in high-dimensional models such as neural networks. Unfo…
Technical Report on Efficient Integration of Dynamic TDD with Massive MIMO
Yan Huang, Brian Jalaian, Stephen Russell +1
Recent advances in massive multiple-input multiple-output (MIMO) communication show that equipping base stations (BSs) with large arrays of antenna can significantly improve the pe…