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04/20/2023
Enhancing object detection robustness: A synthetic and natural perturbation approach
Robustness against real-world distribution shifts is crucial for the suc...
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02/08/2022
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning
Bayesian methods hold significant promise for improving the uncertainty ...
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08/11/2021
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Graph Neural Networks (GNNs) have recently demonstrated superior capabil...
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07/15/2021
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo
Federated learning performed by a decentralized networks of agents is be...
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04/26/2021
Graph Neural Networks with Adaptive Frequency Response Filter
Graph Neural Networks have recently become a prevailing paradigm for var...
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12/11/2020
Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization
In this paper, we propose a surrogate-assisted evolutionary algorithm (E...
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10/14/2020
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) appro...
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07/08/2020
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
While deep learning methods continue to improve in predictive accuracy o...
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05/16/2020
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
In this paper, we present a general framework for distilling expectation...
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02/07/2020
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification
In this paper, we consider the problem of assessing the adversarial robu...
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05/07/2019
Are Graph Neural Networks Miscalibrated?
Graph Neural Networks (GNNs) have proven to be successful in many classi...
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03/14/2019