40 citations · 41 across the 6 of their papers we have counts for
6 papers
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
Yeachan Kim, Junho Kim, Wing-Lam Mok +2
Despite the versatility of pre-trained language models (PLMs) across domains, their large memory footprints pose significant challenges in federated learning (FL), where the traini…
Dynamic Structure Pruning for Compressing CNNs
Jun-Hyung Park, Yeachan Kim, Junho Kim +2
Structure pruning is an effective method to compress and accelerate neural networks. While filter and channel pruning are preferable to other structure pruning methods in terms of…
Efficient Pre-training of Masked Language Model via Concept-based Curriculum Masking
Mingyu Lee, Jun-Hyung Park, Junho Kim +2
Masked language modeling (MLM) has been widely used for pre-training effective bidirectional representations, but incurs substantial training costs. In this paper, we propose a nov…
Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding
Deunsol Yoon, Dongbok Lee, SangKeun Lee
In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabour…
Utilizing Probase in Open Directory Project-based Text Classification
So-Young Jun, Dinara Aliyeva, Ji-Min Lee +1
Open Directory Project (ODP) has been successfully utilized in text classification due to its representation ability of various categories. However, ODP includes a limited number o…
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification
Kang-Min Kim, Aliyeva Dinara, Byung-Ju Choi +1
Recently, implicit representation models, such as embedding or deep learning, have been successfully adopted to text classification task due to their outstanding performance. Howev…