Search Results for author: Sunghun Kim

Found 27 papers, 16 papers with code

Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts

no code implementations7 Apr 2024 Weilin Cai, Juyong Jiang, Le Qin, Junwei Cui, Sunghun Kim, Jiayi Huang

Expert parallelism has been introduced as a strategy to distribute the computational workload of sparsely-gated mixture-of-experts (MoE) models across multiple computing devices, facilitating the execution of these increasingly large-scale models.

Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

1 code implementation17 Mar 2024 Peilin Zhou, You-Liang Huang, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, Sunghun Kim

Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead.

Contrastive Learning Data Augmentation +1

High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs

no code implementations26 Feb 2024 Peiyan Zhang, Chaozhuo Li, Liying Kang, Feiran Huang, Senzhang Wang, Xing Xie, Sunghun Kim

Moreover, we show that existing contrastive objective learns the low-frequency component of the augmentation graph and propose a high-frequency component (HFC)-aware contrastive learning objective that makes the learned embeddings more distinctive.

Contrastive Learning Representation Learning

Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models

no code implementations21 Aug 2023 Peiyan Zhang, Haoyang Liu, Chaozhuo Li, Xing Xie, Sunghun Kim, Haohan Wang

Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in the real world is still in discussion.

Image Classification

Attention Calibration for Transformer-based Sequential Recommendation

1 code implementation18 Aug 2023 Peilin Zhou, Qichen Ye, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, Chenyu You, Sunghun Kim

Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations.

Sequential Recommendation

Continual Learning on Dynamic Graphs via Parameter Isolation

1 code implementation23 May 2023 Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, Sunghun Kim

Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.

Continual Learning Graph Learning

A Survey on Incremental Update for Neural Recommender Systems

no code implementations6 Mar 2023 Peiyan Zhang, Sunghun Kim

In this article, we offer a systematic survey of incremental update for neural recommender systems.

Recommendation Systems

Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems

1 code implementation28 Feb 2023 Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jaeboum Kim, Fangzhao Wu, Sunghun Kim

To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method.

Recommendation Systems

Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization

no code implementations10 Nov 2022 Yueqi Xie, Weizhong Zhang, Renjie Pi, Fangzhao Wu, Qifeng Chen, Xing Xie, Sunghun Kim

Since at each round, the number of tunable parameters optimized on the server side equals the number of participating clients (thus independent of the model size), we are able to train a global model with massive parameters using only a small amount of proxy data (e. g., around one hundred samples).

Federated Learning

Equivariant Contrastive Learning for Sequential Recommendation

1 code implementation10 Nov 2022 Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jae Boum Kim, Shoujin Wang, Sunghun Kim

Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e. g., item substitution) and insensitive to mild augmentations (e. g., featurelevel dropout masking).

Contrastive Learning Data Augmentation +1

Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting

2 code implementations5 Aug 2022 Juyong Jiang, Binqing Wu, Ling Chen, Kai Zhang, Sunghun Kim

On the one hand, our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions; on the other hand, it uses GAN structure to systematically evaluate statistical consistencies between the real and the predicted time series in terms of both the temporal trending and the complex spatial-temporal dependencies.

Time Series Time Series Analysis

Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network

1 code implementation26 Jun 2022 Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, Sunghun Kim

Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.

Session-Based Recommendations

Decoupled Side Information Fusion for Sequential Recommendation

1 code implementation23 Apr 2022 Yueqi Xie, Peilin Zhou, Sunghun Kim

Motivated by this, we propose Decoupled Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the side information from the input to the attention layer and decouples the attention calculation of various side information and item representation.

Attribute Representation Learning +1

Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training

1 code implementation13 Dec 2021 Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jaeboum Kim, Kai Zhang, Senzhang Wang, Sunghun Kim

Our approach leverages bidirectional temporal augmentation and knowledge-enhanced fine-tuning to synthesize authentic pseudo-prior items that \emph{retain user preferences and capture deeper item semantic correlations}, thus boosting the model's expressive power.

Data Augmentation Self-Knowledge Distillation +1

NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions

no code implementations IJCNLP 2019 Fuxiang Chen, Seung-won Hwang, Jaegul Choo, Jung-Woo Ha, Sunghun Kim

Here we describe a new NL2pSQL task to generate pSQL codes from natural language questions on under-specified database issues, NL2pSQL.


NSML: A Machine Learning Platform That Enables You to Focus on Your Models

no code implementations16 Dec 2017 Nako Sung, Minkyu Kim, Hyunwoo Jo, Youngil Yang, Jingwoong Kim, Leonard Lausen, Youngkwan Kim, Gayoung Lee, Dong-Hyun Kwak, Jung-Woo Ha, Sunghun Kim

However, researchers are still required to perform a non-trivial amount of manual tasks such as GPU allocation, training status tracking, and comparison of models with different hyperparameter settings.

BIG-bench Machine Learning

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

34 code implementations CVPR 2018 Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, Jaegul Choo

To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.

 Ranked #1 on Image-to-Image Translation on RaFD (using extra training data)

Attribute Image-to-Image Translation +1

DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning

no code implementations25 Apr 2017 Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim

They rely on the sparse availability of bilingual projects, thus producing a limited number of API mappings.

Deep API Learning

no code implementations27 May 2016 Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim

We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query.

Decoder Information Retrieval +3

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