1 citations · 2 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…
DIVE: Towards Descriptive and Diverse Visual Commonsense Generation
Jun-Hyung Park, Hyuntae Park, Youjin Kang +2
Towards human-level visual understanding, visual commonsense generation has been introduced to generate commonsense inferences beyond images. However, current research on visual co…
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction
Jun-Hyung Park, Yeachan Kim, Mingyu Lee +2
Chemical representation learning has gained increasing interest due to the limited availability of supervised data in fields such as drug and materials design. This interest partic…
Products, Abstractions and Inclusions of Causal Spaces
Simon Buchholz, Junhyung Park, Bernhard Schölkopf
Causal spaces have recently been introduced as a measure-theoretic framework to encode the notion of causality. While it has some advantages over established frameworks, such as st…
A continuous Structural Intervention Distance to compare Causal Graphs
Mihir Dhanakshirur, Felix Laumann, Junhyung Park +1
Understanding and adequately assessing the difference between a true and a learnt causal graphs is crucial for causal inference under interventions. As an extension to the graph-ba…
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…