297 citations · 347 across the 9 of their papers we have counts for
9 papers
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology
Bongjoon Hyun, Taehun Kim, Dongjae Lee +1
Processing-in-memory (PIM) has been explored for decades by computer architects, yet it has never seen the light of day in real-world products due to their high design overheads an…
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
Yuan He, Jiaoyan Chen, Hang Dong +4
Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need…
Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters
Hyunsik Jeon, Jun-Gi Jang, Taehun Kim +1
How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted wid…
SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning
Eunkyu Oh, Taehun Kim, Minsoo Kim +2
Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. The previous works have been modeling the session as a variable-length of a seque…
STING: Self-attention based Time-series Imputation Networks using GAN
Eunkyu Oh, Taehun Kim, Yunhu Ji +1
Time series data are ubiquitous in real-world applications. However, one of the most common problems is that the time series data could have missing values by the inherent nature o…
UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation
Taehun Kim, Hyemin Lee, Daijin Kim
We propose Uncertainty Augmented Context Attention network (UACANet) for polyp segmentation which consider a uncertain area of the saliency map. We construct a modified version of…
SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous Convolution Consensus for Semantic Segmentation
Taehun Kim, Jinseong Kim, Daijin Kim
Semantic segmentation networks adopt transfer learning from image classification networks which occurs a shortage of spatial context information. For this reason, we propose Spatia…
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations
Ranggi Hwang, Taehun Kim, Youngeun Kwon +1
Personalized recommendations are the backbone machine learning (ML) algorithm that powers several important application domains (e.g., ads, e-commerce, etc) serviced from cloud dat…
Fast and Accurate Transferability Measurement for Heterogeneous Multivariate Data
Seungcheol Park, Huiwen Xu, Taehun Kim +3
Given a set of heterogeneous source datasets with their classifiers, how can we quickly find the most useful source dataset for a specific target task? We address the problem of me…