30 citations · 36 across the 3 of their papers we have counts for
10 papers
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient
Vu C. Dinh, Lam Si Tung Ho, Cuong V. Nguyen
We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of…
Simple Transferability Estimation for Regression Tasks
Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho +4
We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which receive…
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Matthew Ashman, Thang D. Bui, Cuong V. Nguyen +4
The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional alg…
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Cuong V. Nguyen, Tal Hassner, Matthias Seeger +1
We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy…
Transferability and Hardness of Supervised Classification Tasks
Anh T. Tran, Cuong V. Nguyen, Tal Hassner
We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does n…
Toward Understanding Catastrophic Forgetting in Continual Learning
Cuong V. Nguyen, Alessandro Achille, Michael Lam +3
We study the relationship between catastrophic forgetting and properties of task sequences. In particular, given a sequence of tasks, we would like to understand which properties o…