Search Results for author: Brian Jalaian

Found 12 papers, 7 papers with code

Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning

no code implementations8 Feb 2022 Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin

In this paper, we investigate the potential of sparse network structures to flexibly trade-off model storage costs and inference run time against predictive performance and uncertainty quantification ability.

Bayesian Inference

EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks

1 code implementation11 Aug 2021 Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li

We then develop a framework EDITS to mitigate the bias in attributed networks while maintaining the performance of GNNs in downstream tasks.

Decision Making Fraud Detection

Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo

no code implementations15 Jul 2021 Vyacheslav Kungurtsev, Adam Cobb, Tara Javidi, Brian Jalaian

Federated learning performed by a decentralized networks of agents is becoming increasingly important with the prevalence of embedded software on autonomous devices.

Federated Learning

Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization

1 code implementation11 Dec 2020 Subhodip Biswas, Adam D Cobb, Andreea Sistrunk, Naren Ramakrishnan, Brian Jalaian

In this paper, we propose a surrogate-assisted evolutionary algorithm (EA) for hyperparameter optimization of machine learning (ML) models.

Hyperparameter Optimization

Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting

1 code implementation14 Oct 2020 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.

URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

1 code implementation8 Jul 2020 Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin

In this paper, we describe initial work on the development ofURSABench(the Uncertainty, Robustness, Scalability, and Accu-racy Benchmark), an open-source suite of bench-marking tools for comprehensive assessment of approximate Bayesian inference methods with a focus on deep learning-based classification tasks

Bayesian Inference

Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks

no code implementations16 May 2020 Meet P. Vadera, Brian Jalaian, Benjamin M. Marlin

In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network classifier, extending prior work on the Bayesian Dark Knowledge framework.

Out-of-Distribution Detection

Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification

no code implementations7 Feb 2020 Meet P. Vadera, Satya Narayan Shukla, Brian Jalaian, Benjamin M. Marlin

In this paper, we consider the problem of assessing the adversarial robustness of deep neural network models under both Markov chain Monte Carlo (MCMC) and Bayesian Dark Knowledge (BDK) inference approximations.

Adversarial Robustness General Classification

Are Graph Neural Networks Miscalibrated?

1 code implementation7 May 2019 Leonardo Teixeira, Brian Jalaian, Bruno Ribeiro

Graph Neural Networks (GNNs) have proven to be successful in many classification tasks, outperforming previous state-of-the-art methods in terms of accuracy.

Decision Making General Classification

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