9 citations · 18 across the 10 of their papers we have counts for
11 papers
Explainable Product Classification for Customs
Eunji Lee, Sihyeon Kim, Sundong Kim +3
The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this t…
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis
Sungwon Han, Sungwon Park, Fangzhao Wu +4
Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other. Federated learning systems are susceptible to poison…
FedDefender: Client-Side Attack-Tolerant Federated Learning
Sungwon Park, Sungwon Han, Fangzhao Wu +4
Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning a…
Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer
Jaehyun Park, Jaegyun Im, Sanha Hwang +4
In the pursuit of artificial general intelligence (AGI), we tackle Abstraction and Reasoning Corpus (ARC) tasks using a novel two-pronged approach. We employ the Decision Transform…
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Sungwon Han, Seungeon Lee, Fangzhao Wu +5
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair…
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
Sungwon Han, Sungwon Park, Fangzhao Wu +4
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased representation from decentralized and heterogeneous local data. It employs a two-s…
Improving Unsupervised Image Clustering With Robust Learning
Sungwon Park, Sungwon Han, Sundong Kim +4
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overc…
Disruption in the Chinese E-Commerce During COVID-19
Yuan Yuan, Muzhi Guan, Zhilun Zhou +4
The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines its impact on the Chinese e-commerce…
Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection
Hwanjun Song, Minseok Kim, Sundong Kim +1
The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch s…
Behavior of Self-Motivated Agents in Complex Networks
Sundong Kim, Jin-Jae Lee
Traditional evolutionary game theory describes how certain strategy spreads throughout the system where individual player imitates the most successful strategy among its neighborho…
Automatic Knowledge Base Evolution by Learning Instances
Sundong Kim
Knowledge base is the way to store structured and unstructured data throughout the web. Since the size of the web is increasing rapidly, there are huge needs to structure the knowl…