1 code implementation • 9 Jan 2025 • Hyeonsoo Jo, Hyunjin Hwang, Fanchen Bu, Soo Yong Lee, Chanyoung Park, Kijung Shin
First, to mitigate the bypass problem, HideNSeek learns to distinguish the original and (potential) attack edges using a learnable edge scorer (LEO), which scores each edge on its likelihood of being an attack.
1 code implementation • 17 Dec 2024 • Kanghoon Yoon, Kibum Kim, Jaehyung Jeon, Yeonjun In, Donghyun Kim, Chanyoung Park
Scene Graph Generation (SGG) research has suffered from two fundamental challenges: the long-tailed predicate distribution and semantic ambiguity between predicates.
1 code implementation • 4 Dec 2024 • Namkyeong Lee, Yunhak Oh, Heewoong Noh, Gyoung S. Na, Minkai Xu, Hanchen Wang, Tianfan Fu, Chanyoung Park
Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery.
1 code implementation • 19 Nov 2024 • JungHoon Kim, Junmo Lee, Yeonjun In, Kanghoon Yoon, Chanyoung Park
In this work, we newly formalize a more realistic evaluation scheme that mimics real-world scenarios, where the data is temporality-aware and the detection model can only be trained on data collected up to a certain point in time.
1 code implementation • 28 Oct 2024 • Heewoong Noh, Namkyeong Lee, Gyoung S. Na, Chanyoung Park
In this paper, we propose Retrieval-Retro for inorganic retrosynthesis planning, which implicitly extracts the precursor information of reference materials that are retrieved from the knowledge base regarding domain expertise in the field.
no code implementations • 4 Sep 2024 • Yeonjun In, Sungchul Kim, Ryan A. Rossi, Md Mehrab Tanjim, Tong Yu, Ritwik Sinha, Chanyoung Park
The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generating comprehensive responses based on the passages.
1 code implementation • 27 Jul 2024 • Kanghoon Yoon, Yeonjun In, Namkyeong Lee, Kibum Kim, Chanyoung Park
That is, their meta-gradient is determined by a training procedure of the surrogate model, which is solely trained on the training nodes.
1 code implementation • 25 Jul 2024 • Sukwon Yun, Jie Peng, Alexandro E. Trevino, Chanyoung Park, Tianlong Chen
Recent advancements in graph-based approaches for multiplexed immunofluorescence (mIF) images have significantly propelled the field forward, offering deeper insights into patient-level phenotyping.
1 code implementation • 22 Jul 2024 • Jaehyeong Jeon, Kibum Kim, Kanghoon Yoon, Chanyoung Park
The scene graph generation (SGG) task involves detecting objects within an image and predicting predicates that represent the relationships between the objects.
Ranked #2 on
Unbiased Scene Graph Generation
on Visual Genome
1 code implementation • 12 Jul 2024 • Namkyeong Lee, Siddhartha Laghuvarapu, Chanyoung Park, Jimeng Sun
This is because unique challenges exist apart from VLM in the field of MoLM due to 1) a limited amount of molecule-text paired data and 2) missing expertise that occurred due to the specialized areas of focus among the experts.
1 code implementation • 19 Jun 2024 • Sangwoo Seo, Sungwon Kim, Jihyeong Jung, Yoonho Lee, Chanyoung Park
In this work, we propose a novel built-in explanation framework for temporal graphs, called Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck (TGIB).
no code implementations • 5 Jun 2024 • SangHyun Lee, Chanyoung Park
The real-world traffic networks undergo expansion through the installation of new sensors, implying that the traffic patterns continually evolve over time.
no code implementations • 4 Jun 2024 • SangHyun Lee, Chanyoung Park
However, we observe that the spatial dependencies between roads indeed change over time, and two distant roads are not likely to be helpful to each other when predicting the traffic flow, both of which limit the performance of existing studies.
1 code implementation • 17 Apr 2024 • Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, MinChul Yang, Chanyoung Park
Despite their effectiveness under cold scenarios, we observe that they underperform simple traditional collaborative filtering models under warm scenarios due to the lack of collaborative knowledge.
1 code implementation • 22 Feb 2024 • Wonjoong Kim, Sangwu Park, Yeonjun In, Seokwon Han, Chanyoung Park
Recently, interpreting complex charts with logical reasoning has emerged as challenges due to the development of vision-language models.
1 code implementation • 21 Feb 2024 • Seungyoon Choi, Wonjoong Kim, Sungwon Kim, Yeonjun In, Sein Kim, Chanyoung Park
We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods.
1 code implementation • 19 Feb 2024 • Yeonjun In, Kanghoon Yoon, Kibum Kim, Kijung Shin, Chanyoung Park
However, we have discovered that existing GSR methods are limited by narrowassumptions, such as assuming clean node features, moderate structural attacks, and the availability of external clean graphs, resulting in the restricted applicability in real-world scenarios.
1 code implementation • 18 Jan 2024 • Kibum Kim, Kanghoon Yoon, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park
To this end, we introduce a Self-Training framework for SGG (ST-SGG) that assigns pseudo-labels for unannotated triplets based on which the SGG models are trained.
1 code implementation • 17 Nov 2023 • Namkyeong Lee, Heewoong Noh, Gyoung S. Na, Jimeng Sun, Tianfan Fu, Marinka Zitnik, Chanyoung Park
Machine learning (ML) has seen promising developments in materials science, yet its efficacy largely depends on detailed crystal structural data, which are often complex and hard to obtain, limiting their applicability in real-world material synthesis processes.
no code implementations • 31 Oct 2023 • Ju-Hyung Lee, Chanyoung Park, Soohyun Park, Andreas F. Molisch
This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks' HO procedures.
1 code implementation • NeurIPS 2023 • Sangwoo Seo, Sungwon Kim, Chanyoung Park
In this work, we propose a novel framework of explainable GNNs, called interpretable Prototype-based Graph Information Bottleneck (PGIB) that incorporates prototype learning within the information bottleneck framework to provide prototypes with the key subgraph from the input graph that is important for the model prediction.
1 code implementation • NeurIPS 2023 • Namkyeong Lee, Heewoong Noh, Sungwon Kim, Dongmin Hyun, Gyoung S. Na, Chanyoung Park
While previous works mainly focus on obtaining high-quality representations of crystalline materials for DOS prediction, we focus on predicting the DOS from the obtained representations by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy.
1 code implementation • CVPR 2024 • Kibum Kim, Kanghoon Yoon, Jaehyeong Jeon, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park
Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations.
1 code implementation • 22 Aug 2023 • JungHoon Kim, Yeonjun In, Kanghoon Yoon, Junmo Lee, Chanyoung Park
Unsupervised GAD methods assume the lack of anomaly labels, i. e., whether a node is anomalous or not.
1 code implementation • 18 Aug 2023 • Yunhak Oh, Sukwon Yun, Dongmin Hyun, Sein Kim, Chanyoung Park
Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music.
1 code implementation • 16 Aug 2023 • Junghurn Kim, Sukwon Yun, Chanyoung Park
Existing studies for applying the mixup technique on graphs mainly focus on graph classification tasks, while the research in node classification is still under-explored.
no code implementations • 3 Aug 2023 • Soohyun Park, Jae Pyoung Kim, Chanyoung Park, Soyi Jung, Joongheon Kim
To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediate-scale quantum (NISQ) era.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
no code implementations • 19 Jul 2023 • Soohyun Park, Haemin Lee, Chanyoung Park, Soyi Jung, Minseok Choi, Joongheon Kim
This paper presents the deep learning-based recent achievements to resolve the problem of autonomous mobility control and efficient resource management of autonomous vehicles and UAVs, i. e., (i) multi-agent reinforcement learning (MARL), and (ii) neural Myerson auction.
1 code implementation • 27 Jun 2023 • Jihyeong Jung, Sangwoo Seo, Sungwon Kim, Chanyoung Park
We propose Unsupervised Episode Generation method called Neighbors as Queries (NaQ) to solve the Few-Shot Node-Classification (FSNC) task by unsupervised Graph Meta-learning.
1 code implementation • 24 Jun 2023 • Yeonjun In, Kanghoon Yoon, Chanyoung Park
Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which refer to imperceptible perturbation on the graph structure and node features.
1 code implementation • 1 Jun 2023 • Sein Kim, Namkyeong Lee, Donghyun Kim, MinChul Yang, Chanyoung Park
However, since learning task-specific user representations for every task is infeasible, recent studies introduce the concept of universal user representation, which is a more generalized representation of a user that is relevant to a variety of tasks.
1 code implementation • 30 May 2023 • Sungwon Kim, Junseok Lee, Namkyeong Lee, Wonjoong Kim, Seungyoon Choi, Chanyoung Park
To solve this problem, it is important for GNNs to be able to classify nodes with a limited number of labeled nodes, known as few-shot node classification.
1 code implementation • 29 May 2023 • Namkyeong Lee, Kanghoon Yoon, Gyoung S. Na, Sein Kim, Chanyoung Park
To do so, we first assume a causal relationship based on the domain knowledge of molecular sciences and construct a structural causal model (SCM) that reveals the relationship between variables.
1 code implementation • 29 Apr 2023 • Namkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim, Junseok Lee, Chanyoung Park
Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications.
1 code implementation • 17 Apr 2023 • Kibum Kim, Dongmin Hyun, Sukwon Yun, Chanyoung Park
The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items.
1 code implementation • 13 Mar 2023 • Namkyeong Lee, Heewoong Noh, Sungwon Kim, Dongmin Hyun, Gyoung S. Na, Chanyoung Park
The density of states (DOS) is a spectral property of materials, which provides fundamental insights on various characteristics of materials.
no code implementations • 9 Feb 2023 • Chanyoung Park, Won Joon Yun, Jae Pyoung Kim, Tiago Koketsu Rodrigues, Soohyun Park, Soyi Jung, Joongheon Kim
This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs).
no code implementations • 25 Jan 2023 • Baoyu Jing, Yuchen Yan, Kaize Ding, Chanyoung Park, Yada Zhu, Huan Liu, Hanghang Tong
Most recent bipartite graph SSL methods are based on contrastive learning which learns embeddings by discriminating positive and negative node pairs.
no code implementations • 23 Dec 2022 • Chanyoung Park, Haemin Lee, Won Joon Yun, Soyi Jung, Joongheon Kim
This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+1
1 code implementation • 21 Dec 2022 • Dongmin Hyun, Xiting Wang, Chanyoung Park, Xing Xie, Hwanjo Yu
We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality.
1 code implementation • 1 Dec 2022 • Kanghoon Yoon, Kibum Kim, Jinyoung Moon, Chanyoung Park
Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image.
1 code implementation • 28 Nov 2022 • Sein Kim, Namkyeong Lee, Junseok Lee, Dongmin Hyun, Chanyoung Park
In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data.
no code implementations • 24 Nov 2022 • Chanyoung Park, Jae Pyoung Kim, Won Joon Yun, Soohyun Park, Soyi Jung, Joongheon Kim
Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL).
Multi-agent Reinforcement Learning
Quantum Machine Learning
+3
no code implementations • 13 Nov 2022 • Chanyoung Park, Soohyun Park, Gyu Seon Kim, Soyi Jung, Jae-Hyun Kim, Joongheon Kim
It has been considered that urban air mobility (UAM), also known as drone-taxi or electrical vertical takeoff and landing (eVTOL), will play a key role in future transportation.
1 code implementation • 7 Oct 2022 • Geon Lee, Chanyoung Park, Kijung Shin
Through extensive experiments on 8 real-world datasets, we show that, compared to baseline approaches, Set2Box+ is (a) Accurate: achieving up to 40. 8X smaller estimation error while requiring 60% fewer bits to encode sets, (b) Concise: yielding up to 96. 8X more concise representations with similar estimation error, and (c) Versatile: enabling the estimation of four set-similarity measures from a single representation of each set.
no code implementations • 3 Oct 2022 • Chanyoung Park, Soohyun Park, Soyi Jung, Carlos Cordeiro, Joongheon Kim
The reliable mobile access services can be achieved in following two ways, i. e., i) energy-efficient UAV operation and ii) reliable wireless communication services.
1 code implementation • 14 Sep 2022 • Dongmin Hyun, Chanyoung Park, Junsu Cho, Hwanjo Yu
We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history.
Ranked #1 on
Sequential Recommendation
on Amazon Cell Phones
1 code implementation • 22 Aug 2022 • Sukwon Yun, Kibum Kim, Kanghoon Yoon, Chanyoung Park
After having trained an expert for each balanced subset, we adopt knowledge distillation to obtain two class-wise students, i. e., Head class student and Tail class student, each of which is responsible for classifying nodes in the head classes and tail classes, respectively.
1 code implementation • 21 Aug 2022 • Namkyeong Lee, Dongmin Hyun, Junseok Lee, Chanyoung Park
Despite their success, existing GRL methods tend to overlook an inherent distinction between images and graphs, i. e., images are assumed to be independently and identically distributed, whereas graphs exhibit relational information among data instances, i. e., nodes.
1 code implementation • 13 Apr 2022 • Hyunjin Hwang, Seungwoo Lee, Chanyoung Park, Kijung Shin
Since it is prohibitive to use all of them as negative examples for model training, it is inevitable to sample a very small portion of them, and to this end, heuristic sampling schemes have been employed.
2 code implementations • 4 Apr 2022 • Junseok Lee, Yunhak Oh, Yeonjun In, Namkyeong Lee, Dongmin Hyun, Chanyoung Park
Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i. e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes.
no code implementations • 7 Mar 2022 • Qi Zhu, Chao Zhang, Chanyoung Park, Carl Yang, Jiawei Han
Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN.
1 code implementation • 5 Dec 2021 • Namkyeong Lee, Junseok Lee, Chanyoung Park
Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods.
no code implementations • 22 Nov 2021 • Dongha Lee, Su Kim, Seonghyeon Lee, Chanyoung Park, Hwanjo Yu
By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation.
no code implementations • 13 May 2021 • Dongha Lee, SeongKu Kang, Hyunjun Ju, Chanyoung Park, Hwanjo Yu
To make the representations of positively-related users and items similar to each other while avoiding a collapsed solution, BUIR adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the second encoder as its target, while the second encoder provides the consistent targets by slowly approximating the first encoder.
1 code implementation • 15 Feb 2021 • Baoyu Jing, Chanyoung Park, Hanghang Tong
To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way.
1 code implementation • Conference 2020 • Dongmin Hyun, Junsu Cho, Chanyoung Park, Hwanjo Yu
More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future.
1 code implementation • 7 Jun 2020 • Chanyoung Park, Carl Yang, Qi Zhu, Donghyun Kim, Hwanjo Yu, Jiawei Han
To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.
no code implementations • 26 Nov 2019 • Seonghyeon Lee, Chanyoung Park, Hwanjo Yu
We view the heterogeneous network embedding as simultaneously solving multiple tasks in which each task corresponds to each relation type in a network.
2 code implementations • 15 Nov 2019 • Chanyoung Park, Donghyun Kim, Jiawei Han, Hwanjo Yu
Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph.
1 code implementation • 4 Jun 2019 • Chanyoung Park, Donghyun Kim, Xing Xie, Hwanjo Yu
We also conduct extensive qualitative evaluations on the translation vectors learned by our proposed method to ascertain the benefit of adopting the translation mechanism for implicit feedback-based recommendations.
Ranked #1 on
Recommendation Systems
on Declicious
1 code implementation • 4 Jun 2019 • Chanyoung Park, Donghyun Kim, Qi Zhu, Jiawei Han, Hwanjo Yu
In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e. g., paper-author relationship in author identification).
no code implementations • DSTC Workshop 2017 • Chanyoung Park, Kyungduk Kim, Songkuk Kim
Dialog Breakdown Detection Challenge 3 of Dialog System Technology Challenge 6
no code implementations • 21 Jun 2017 • Chanyoung Park, Donghyun Kim, Min-Chul Yang, Jung-Tae Lee, Hwanjo Yu
We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records.
1 code implementation • 23 Sep 2016 • M. Giselle Fernández-Godino, Chanyoung Park, Nam-Ho Kim, Raphael T. Haftka
Multi-fidelity models provide a framework for integrating computational models of varying complexity, allowing for accurate predictions while optimizing computational resources.
Applications 65C99, 65D15, 68W25, 76-00, 74-00 A.1; G.3; I.6.5; I.2.8
1 code implementation • RecSys 2016 • Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, Hwanjo Y
However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents.