23 citations · 61 across the 11 of their papers we have counts for
7 papers · 1 filter
Soft-TransFormers for Continual Learning
Haeyong Kang, Chang D. Yoo
Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-TransFormers (Soft-TF), a continual learning framework that adapts a frozen pre-trained Transfo…
On the Perturbed States for Transformed Input-robust Reinforcement Learning
Tung M. Luu, Haeyong Kang, Tri Ton +2
Reinforcement Learning (RL) agents demonstrating proficiency in a training environment exhibit vulnerability to adversarial perturbations in input observations during deployment. T…
Forget-free Continual Learning with Soft-Winning SubNetworks
Haeyong Kang, Jaehong Yoon, Sultan Rizky Madjid +2
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we…
Maximum margin learning of t-SPNs for cell classification with filtered input
Haeyong Kang, Chang D. Yoo, Yongcheon Na
An algorithm based on a deep probabilistic architecture referred to as a tree-structured sum-product network (t-SPN) is considered for cell classification. The t-SPN is constructed…
Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation
Haeyong Kang, Chang D. Yoo
An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the…
On the Soft-Subnetwork for Few-shot Class Incremental Learning
Haeyong Kang, Jaehong Yoon, Sultan Rizky Hikmawan Madjid +2
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive pe…
Learning Imbalanced Datasets with Maximum Margin Loss
Haeyong Kang, Thang Vu, Chang D. Yoo
A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of clas…