Hwanjun Song

Orcid: 0000-0002-1105-0818

According to our database1, Hwanjun Song authored at least 50 papers between 2017 and 2024.

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Bibliography

2024
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders.
CoRR, 2024

Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection.
CoRR, 2024

MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets.
CoRR, 2024

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization.
CoRR, 2024

Toward Robustness in Multi-Label Classification: A Data Augmentation Strategy against Imbalance and Noise.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Adaptive Shortcut Debiasing for Online Continual Learning.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Learning From Noisy Labels With Deep Neural Networks: A Survey.
IEEE Trans. Neural Networks Learn. Syst., November, 2023

Data collection and quality challenges in deep learning: a data-centric AI perspective.
VLDB J., July, 2023

One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning.
CoRR, 2023

Prompt-Guided Transformers for End-to-End Open-Vocabulary Object Detection.
CoRR, 2023

Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction.
CoRR, 2023

Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Context Consistency Regularization for Label Sparsity in Time Series.
Proceedings of the International Conference on Machine Learning, 2023

Online Boundary-Free Continual Learning by Scheduled Data Prior.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Generating Instance-level Prompts for Rehearsal-free Continual Learning.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Enhancing Abstractiveness of Summarization Models through Calibrated Distillation.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Re-Thinking Federated Active Learning Based on Inter-Class Diversity.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
An Extendable, Efficient and Effective Transformer-based Object Detector.
CoRR, 2022

Understanding Cross-Domain Few-Shot Learning: An Experimental Study.
CoRR, 2022

Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Time Is MattEr: Temporal Self-supervision for Video Transformers.
Proceedings of the International Conference on Machine Learning, 2022

Dataset Condensation via Efficient Synthetic-Data Parameterization.
Proceedings of the International Conference on Machine Learning, 2022

ViDT: An Efficient and Effective Fully Transformer-based Object Detector.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Coherence-based Label Propagation over Time Series for Accelerated Active Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Multi-view POI-level Cellular Trajectory Reconstruction for Digital Contact Tracing of Infectious Diseases.
Proceedings of the IEEE International Conference on Data Mining, 2022

Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Meta-Learning for Online Update of Recommender Systems.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Robust Learning by Self-Transition for Handling Noisy Labels.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Machine Learning Robustness, Fairness, and their Convergence.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Exploiting Scene Depth for Object Detection with Multimodal Transformers.
Proceedings of the 32nd British Machine Vision Conference 2021, 2021

PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Ada-boundary: accelerating DNN training via adaptive boundary batch selection.
Mach. Learn., 2020

Two-Phase Learning for Overcoming Noisy Labels.
CoRR, 2020

Learning from Noisy Labels with Deep Neural Networks: A Survey.
CoRR, 2020

TRAP: Two-level Regularized Autoencoder-based Embedding for Power-law Distributed Data.
Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

Revisit Prediction by Deep Survival Analysis.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2020

Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Carpe Diem, Seize the Samples Uncertain "at the Moment" for Adaptive Batch Selection.
Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2019
Prestopping: How Does Early Stopping Help Generalization against Label Noise?
CoRR, 2019

MLAT: Metric Learning for kNN in Streaming Time Series.
CoRR, 2019

SELFIE: Refurbishing Unclean Samples for Robust Deep Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning.
Proceedings of the 2018 International Conference on Management of Data, 2018

2017
PAMAE: Parallel <i>k</i>-Medoids Clustering with High Accuracy and Efficiency.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017


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