23 citations · 61 across the 11 of their papers we have counts for
12 papers
Budgeted Cache Repair for Cross-Context KV-Cache Reuse
Haeyong Kang, Chang D. Yoo
Cross-context KV-cache reuse predicts a shared segment's keys and values under a new prefix instead of recomputing them, and has been reported to do so without quality loss. We fin…
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…
Continual Learning: Forget-free Winning Subnetworks for Video Representations
Haeyong Kang, Jaehong Yoon, Sung Ju Hwang +1
Inspired by the Lottery Ticket Hypothesis (LTH), which highlights the existence of efficient subnetworks within larger, dense networks, a high-performing Winning Subnetwork (WSN) i…
Progressive Fourier Neural Representation for Sequential Video Compilation
Haeyong Kang, Jaehong Yoon, DaHyun Kim +2
Neural Implicit Representation (NIR) has recently gained significant attention due to its remarkable ability to encode complex and high-dimensional data into representation space a…
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…
Semantic Grouping Network for Video Captioning
Hobin Ryu, Sunghun Kang, Haeyong Kang +1
This paper considers a video caption generating network referred to as Semantic Grouping Network (SGN) that attempts (1) to group video frames with discriminating word phrases of p…
SCNet: Training Inference Sample Consistency for Instance Segmentation
Thang Vu, Haeyong Kang, Chang D. Yoo
Cascaded architectures have brought significant performance improvement in object detection and instance segmentation. However, there are lingering issues regarding the disparity i…