1 citations · 1 across the 5 of their papers we have counts for
12 papers
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy
Cuong N. Nguyen, Lam Si Tung Ho, Vu Dinh +2
We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predict…
Evolutionary shift detection with ensemble variable selection
Wensha Zhang, Toby Kenney, Lam Si Tung Ho
1. Abrupt environmental changes can lead to evolutionary shifts in trait evolution. Identifying these shifts is an important step in understanding the evolutionary history of pheno…
Searching for Minimal Optimal Neural Networks
Lam Si Tung Ho, Vu Dinh
Large neural network models have high predictive power but may suffer from overfitting if the training set is not large enough. Therefore, it is desirable to select an appropriate…
OASIS: An Active Framework for Set Inversion
Binh T. Nguyen, Duy M. Nguyen, Lam Si Tung Ho +1
In this work, we introduce a novel method for solving the set inversion problem by formulating it as a binary classification problem. Aiming to develop a fast algorithm that can wo…
Convergence of maximum likelihood supertree reconstruction
Lam Si Tung Ho, Vu Dinh
Supertree methods are tree reconstruction techniques that combine several smaller gene trees (possibly on different sets of species) to build a larger species tree. The question of…
Ancestral state reconstruction with large numbers of sequences and edge-length estimation
Lam Si Tung Ho, Edward Susko
Likelihood-based methods are widely considered the best approaches for reconstructing ancestral states. Although much effort has been made to study properties of these methods, pre…