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1,560 results on '"Cho, Sung-Bae"'

1. PANDA: Expanded Width-Aware Message Passing Beyond Rewiring

Author

Choi, Jeongwhan, Park, Sumin, Wi, Hyowon, Cho, Sung-Bae, and Park, Noseong

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-range information. Prior works have proposed a variety of graph rewiring concepts that aim at optimizing the spatial or spectral properties of graphs to promote the signal propagation. However, such approaches inevitably deteriorate the original graph topology, which may lead to a distortion of information flow. To address this, we introduce an expanded width-aware (PANDA) message passing, a new message passing paradigm where nodes with high centrality, a potential source of over-squashing, are selectively expanded in width to encapsulate the growing influx of signals from distant nodes. Experimental results show that our method outperforms existing rewiring methods, suggesting that selectively expanding the hidden state of nodes can be a compelling alternative to graph rewiring for addressing the over-squashing., Comment: Accepted at ICML 2024

Published
2024

2. Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering

Author

Lee, Chaejeong, Choi, Jeongwhan, Wi, Hyowon, Cho, Sung-Bae, and Park, Noseong

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Graph-based collaborative filtering (CF) has emerged as a promising approach in recommendation systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. By considering that they are both sampling tasks, we generate dynamic augmented views and diverse hard negative samples via our unified stochastic sampling framework based on score-based generative models. In our comprehensive evaluations with 6 benchmark datasets, our proposed SCONE significantly improves recommendation accuracy and robustness, and demonstrates the superiority of our approach over existing CF models. Furthermore, we prove the efficacy of user-item specific stochastic sampling for addressing the user sparsity and item popularity issues. The integration of the stochastic sampling and graph-based CF obtains the state-of-the-art in personalized recommendation systems, making significant strides in information-rich environments.

Published
2024

4. RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation

Author

Choi, Jeongwhan, Wi, Hyowon, Lee, Chaejeong, Cho, Sung-Bae, Lee, Dongha, and Park, Noseong

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by using self-supervised signals from raw data. Integration of CL with graph convolutional network (GCN)-based collaborative filterings (CFs) has been explored in recommender systems. However, current CL-based recommendation models heavily rely on low-pass filters and graph augmentations. In this paper, inspired by the reaction-diffusion equation, we propose a novel CL method for recommender systems called the reaction-diffusion graph contrastive learning model (RDGCL). We design our own GCN for CF based on the equations of diffusion, i.e., low-pass filter, and reaction, i.e., high-pass filter. Our proposed CL-based training occurs between reaction and diffusion-based embeddings, so there is no need for graph augmentations. Experimental evaluation on 5 benchmark datasets demonstrates that our proposed method outperforms state-of-the-art CL-based recommendation models. By enhancing recommendation accuracy and diversity, our method brings an advancement in CL for recommender systems., Comment: Jeongwhan Choi and Hyowon Wi are co-first authors with equal contributions

Published
2023

6. Contrastive Learning of Multivariate Gaussian Distributions of Incremental Classes for Continual Learning

Author

Moon, Hyung-Jun, Cho, Sung-Bae, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Val Calvo, Mikel, editor, and Adeli, Hojjat, editor

Published
2024
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10. GREAD: Graph Neural Reaction-Diffusion Networks

Author

Choi, Jeongwhan, Hong, Seoyoung, Park, Noseong, and Cho, Sung-Bae

Subjects
Computer Science - Machine Learning
Abstract

Graph neural networks (GNNs) are one of the most popular research topics for deep learning. GNN methods typically have been designed on top of the graph signal processing theory. In particular, diffusion equations have been widely used for designing the core processing layer of GNNs, and therefore they are inevitably vulnerable to the notorious oversmoothing problem. Recently, a couple of papers paid attention to reaction equations in conjunctions with diffusion equations. However, they all consider limited forms of reaction equations. To this end, we present a reaction-diffusion equation-based GNN method that considers all popular types of reaction equations in addition to one special reaction equation designed by us. To our knowledge, our paper is one of the most comprehensive studies on reaction-diffusion equation-based GNNs. In our experiments with 9 datasets and 28 baselines, our method, called GREAD, outperforms them in a majority of cases. Further synthetic data experiments show that it mitigates the oversmoothing problem and works well for various homophily rates., Comment: Accepted by ICML 2023

Published
2022

11. Blurring-Sharpening Process Models for Collaborative Filtering

Author

Choi, Jeongwhan, Hong, Seoyoung, Park, Noseong, and Cho, Sung-Bae

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods. Being inspired by recent successes of graph filtering-based methods and score-based generative models (SGMs), we present a novel concept of blurring-sharpening process model (BSPM). SGMs and BSPMs share the same processing philosophy that new information can be discovered (e.g., new images are generated in the case of SGMs) while original information is first perturbed and then recovered to its original form. However, SGMs and our BSPMs deal with different types of information, and their optimal perturbation and recovery processes have fundamental discrepancies. Therefore, our BSPMs have different forms from SGMs. In addition, our concept not only theoretically subsumes many existing collaborative filtering models but also outperforms them in terms of Recall and NDCG in the three benchmark datasets, Gowalla, Yelp2018, and Amazon-book. In addition, the processing time of our method is comparable to other fast baselines. Our proposed concept has much potential in the future to be enhanced by designing better blurring (i.e., perturbation) and sharpening (i.e., recovery) processes than what we use in this paper., Comment: Accepted by SIGIR 2023

Published
2022

12. TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

Author

Hong, Seoyoung, Jo, Minju, Kook, Seungji, Jung, Jaeeun, Wi, Hyowon, Park, Noseong, and Cho, Sung-Bae

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time-series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time-series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms., Comment: Accepted at IEEE BigData 2022

Published
2022

14. A Subgraph Embedded GIN with Attention for Graph Classification

Author

Moon, Hyung-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Quaresma, Paulo, editor, Camacho, David, editor, Yin, Hujun, editor, Gonçalves, Teresa, editor, Julian, Vicente, editor, and Tallón-Ballesteros, Antonio J., editor

Published
2023
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15. A Causally Explainable Deep Learning Model with Modular Bayesian Network for Predicting Electric Energy Demand

Author

Bu, Seok-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Martínez Álvarez, Francisco, editor, Troncoso Lora, Alicia, editor, Herrero, Álvaro, editor, Calvo Rolle, José Luis, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2023
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16. Phishing URL Detection with Prototypical Neural Network Disentangled by Triplet Sampling

Author

Bu, Seok-Jun, Cho, Sung-Bae, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Martínez Álvarez, Francisco, editor, Troncoso Lora, Alicia, editor, Herrero, Álvaro, editor, Calvo Rolle, José Luis, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2023
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18. CRODNM: Chemical Reaction Optimization of Dendritic Neuron Models for Forecasting Net Asset Values of Mutual Funds

Author

Nayak, Sarat Chandra, Dehuri, Satchidananda, Cho, Sung-Bae, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Panda, Mrutyunjaya, editor, Dehuri, Satchidananda, editor, Patra, Manas Ranjan, editor, Behera, Prafulla Kumar, editor, Tsihrintzis, George A., editor, Cho, Sung-Bae, editor, and Coello Coello, Carlos A., editor

Published
2022
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22. Lifelog Patterns Analyzation using Graph Embedding based on Deep Neural Network

Author

Shin, Wonsup, Kim, Tae-Young, and Cho, Sung-Bae

Subjects
Computer Science - Social and Information Networks, Computer Science - Machine Learning
Abstract

Recently, as the spread of smart devices increases, the amount of data collected through sensors is increasing. A lifelog is a kind of big data to analyze behavior patterns in the daily life of individuals collected from various smart de-vices. However, sensor data is a low-level signal that makes it difficult for hu-mans to recognize the situation directly and cannot express relations clearly. It is also difficult to identify the daily behavior pattern because it records heterogene-ous data by various sensors. In this paper, we propose a method to define a graph structure with node and edge and to extract the daily behavior pattern from the generated lifelog graph. We use the graph convolution method to embeds the lifelog graph and maps it to low dimension. The graph convolution layer im-proves the expressive power of the daily behavior pattern by implanting the life-log graph in the non-Euclidean space and learns the patterns of graphs. Experi-mental results show that the proposed method automatically extracts meaningful user patterns from UbiqLog dataset. In addition, we confirm the usefulness by comparing our method with existing methods to evaluate performance., Comment: 8 pages

Published
2019

23. Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning

Author

Shin, Wonsup, Bu, Seok-Jun, and Cho, Sung-Bae

Subjects
Quantitative Finance - Trading and Market Microstructure, Computer Science - Artificial Intelligence
Abstract

The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account of maximizing returns. Therefore, this paper proposes a deep reinforcement learning based trading agent that can manage the portfolio considering not only profit maximization but also risk restraint. We also propose a new target policy to allow the trading agent to learn to prefer low-risk actions. The new target policy can be reflected in the update by adjusting the greediness for the optimal action through the hyper parameter. The proposed trading agent verifies the performance through the data of the cryptocurrency market. The Cryptocurrency market is the best test-ground for testing our trading agents because of the huge amount of data accumulated every minute and the market volatility is extremely large. As a experimental result, during the test period, our agents achieved a return of 1800% and provided the least risky investment strategy among the existing methods. And, another experiment shows that the agent can maintain robust generalized performance even if market volatility is large or training period is short., Comment: 18 pages

Published
2019

27. A Neuro-Symbolic AI System for Visual Question Answering in Pedestrian Video Sequences

Author

Park, Jaeil, Bu, Seok-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Villar Flecha, José Ramón, editor, Troncoso Lora, Alicia, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Martínez Álvarez, Francisco, editor, Psaila, Giuseppe, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2022
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28. Evolutionary Triplet Network of Learning Disentangled Malware Space for Malware Classification

Author

Park, Kyoung-Won, Bu, Seok-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Villar Flecha, José Ramón, editor, Troncoso Lora, Alicia, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Martínez Álvarez, Francisco, editor, Psaila, Giuseppe, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2022
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29. Anomaly Detection for Health Monitoring of Heavy Equipment Using Hierarchical Prediction with Correlative Feature Learning

Author

Jang, Gye-bong, Cho, Sung-Bae, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sanjurjo González, Hugo, editor, Pastor López, Iker, editor, García Bringas, Pablo, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2022
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30. Mutagenic Prediction for Chemical Compound Discovery with Partitioned Graph Convolution Network

Author

Moon, Hyung-Jun, Bu, Seok-Jun, Cho, Sung-Bae, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sanjurjo González, Hugo, editor, Pastor López, Iker, editor, García Bringas, Pablo, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2022
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32. Directional Graph Transformer-Based Control Flow Embedding for Malware Classification

Author

Moon, Hyung-Jun, Bu, Seok-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yin, Hujun, editor, Camacho, David, editor, Tino, Peter, editor, Allmendinger, Richard, editor, Tallón-Ballesteros, Antonio J., editor, Tang, Ke, editor, Cho, Sung-Bae, editor, Novais, Paulo, editor, and Nascimento, Susana, editor

Published
2021
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33. Learning Dynamic Connectivity with Residual-Attention Network for Autism Classification in 4D fMRI Brain Images

Author

Park, Kyoung-Won, Bu, Seok-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yin, Hujun, editor, Camacho, David, editor, Tino, Peter, editor, Allmendinger, Richard, editor, Tallón-Ballesteros, Antonio J., editor, Tang, Ke, editor, Cho, Sung-Bae, editor, Novais, Paulo, editor, and Nascimento, Susana, editor

Published
2021
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36. Detecting Intrusion via Insider Attack in Database Transactions by Learning Disentangled Representation with Deep Metric Neural Network

Author

Go, Gwang-Myong, Bu, Seok-Jun, Cho, Sung-Bae, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Herrero, Álvaro, editor, Cambra, Carlos, editor, Urda, Daniel, editor, Sedano, Javier, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2021
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37. Interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption

Author

Kim, Jin-Young, Cho, Sung-Bae, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Herrero, Álvaro, editor, Cambra, Carlos, editor, Urda, Daniel, editor, Sedano, Javier, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2021
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38. Evolutionary Optimization of Neuro-Symbolic Integration for Phishing URL Detection

Author

Park, Kyoung-Won, Bu, Seok-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sanjurjo González, Hugo, editor, Pastor López, Iker, editor, García Bringas, Pablo, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2021
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41. Multi-Instance Attention Network for Anomaly Detection from Multivariate Time Series.

Author

Jang, Gye-Bong and Cho, Sung-Bae

Abstract

Anomaly detection and state prediction research using multivariate data is being actively conducted in various industrial fields. However, since most dynamically operating industrial machines perform different operating conditions, they contain different types of abnormal conditions, making it difficult to detect anomalies and predict the remaining life. This white paper proposes a condition diagnosis model based on multi-sensor data prediction for estimating the remaining lifespan of equipment while solving two complex problems of detecting four typical abnormal conditions and sensor omissions of industrial machines. First, we use a multi-sensor data generation model to learn relationships between sensors, and second, we use a sensor data prediction model to learn sensor-specific feature information. In order to extract the temporal and spatial characteristics of sensing information and to derive the relationship between the sensors, we propose an attention model with three types of cases. Finally, the state of the device is diagnosed through the difference between the model predicted value and the actual value, and future state information of the device is predicted through the accumulation of error information. In order to prove the robustness of the proposed model, extensive experiments were conducted focusing on the case where sensor omission occurred due to data from equipment with more than 4 types and conditions. Our model produces missing sensor data with about 92% accuracy and detects anomalies with about 88% accuracy, even if parts of the sensor are missing or the operating environments have been changed. The proposed model has improved anomaly detection accuracy compared to the comparative model, and has been proven to be applicable to real industrial problems. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Classifying Excavator Operations with Fusion Network of Multi-modal Deep Learning Models

Author

Kim, Jin-Young, Cho, Sung-Bae, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Martínez Álvarez, Francisco, editor, Troncoso Lora, Alicia, editor, Sáez Muñoz, José António, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2020
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49. Automated Learning of In-vehicle Noise Representation with Triplet-Loss Embedded Convolutional Beamforming Network

Author

Bu, Seok-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Analide, Cesar, editor, Novais, Paulo, editor, Camacho, David, editor, and Yin, Hujun, editor

Published
2020
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50. A Deep Metric Neural Network with Disentangled Representation for Detecting Smartphone Glass Defects

Author

Go, Gwang-Myong, Bu, Seok-Jun, Cho, Sung-Bae, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Analide, Cesar, editor, Novais, Paulo, editor, Camacho, David, editor, and Yin, Hujun, editor

Published
2020
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