128 citations · 215 across the 8 of their papers we have counts for
8 papers
Enhancing object detection robustness: A synthetic and natural perturbation approach
Nilantha Premakumara, Brian Jalaian, Niranjan Suri +1
Robustness against real-world distribution shifts is crucial for the successful deployment of object detection models in practical applications. In this paper, we address the probl…
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…
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…