Sheng-Jun Huang

Orcid: 0000-0002-7673-5367

According to our database1, Sheng-Jun Huang authored at least 90 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
UNM: A Universal Approach for Noisy Multi-Label Learning.
IEEE Trans. Knowl. Data Eng., September, 2024

A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation.
Mach. Intell. Res., August, 2024

CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs.
CoRR, 2024

Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning.
CoRR, 2024

Relative Difficulty Distillation for Semantic Segmentation.
CoRR, 2024

Improving Generalization of Deep Neural Networks by Optimum Shifting.
CoRR, 2024

Continual Learning in the Presence of Repetition.
CoRR, 2024

Empowering Language Models with Active Inquiry for Deeper Understanding.
CoRR, 2024

Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

NanoAdapt: Mitigating Negative Transfer in Test Time Adaptation with Extremely Small Batch Sizes.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2024

One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Bidirectional Uncertainty-Based Active Learning for Open-Set Annotation.
Proceedings of the Computer Vision - ECCV 2024, 2024

Dirichlet-Based Prediction Calibration for Learning with Noisy Labels.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
QBox: Partial Transfer Learning With Active Querying for Object Detection.
IEEE Trans. Neural Networks Learn. Syst., June, 2023

Learning from crowds with sparse and imbalanced annotations.
Mach. Learn., June, 2023

MUS-CDB: Mixed Uncertainty Sampling With Class Distribution Balancing for Active Annotation in Aerial Object Detection.
IEEE Trans. Geosci. Remote. Sens., 2023

CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise.
IEEE Trans. Pattern Anal. Mach. Intell., 2023

Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ALL-E: Aesthetics-guided Low-light Image Enhancement.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Multi-Label Knowledge Distillation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Partial Multi-Label Learning With Noisy Label Identification.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Improving deep label noise learning with dual active label correction.
Mach. Learn., 2022

Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting.
CoRR, 2022

Meta Objective Guided Disambiguation for Partial Label Learning.
CoRR, 2022

Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Can Adversarial Training Be Manipulated By Non-Robust Features?
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Active Learning for Multiple Target Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Active Learning for Open-set Annotation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

A Tailored Physics-informed Neural Network Method for Solving Singularly Perturbed Differential Equations.
Proceedings of the 5th International Conference on Algorithms, 2022

2021
Label Distribution Learning with Label Correlations on Local Samples.
IEEE Trans. Knowl. Data Eng., 2021

Recent Advances in Open Set Recognition: A Survey.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

PU Active Learning for Recommender Systems.
Neural Process. Lett., 2021

Visual-guided attentive attributes embedding for zero-shot learning.
Neural Networks, 2021

Preface.
J. Comput. Sci. Technol., 2021

Co-Imitation Learning without Expert Demonstration.
CoRR, 2021

Provable Defense Against Delusive Poisoning.
CoRR, 2021

Crowdsourcing aggregation with deep Bayesian learning.
Sci. China Inf. Sci., 2021

Multi-Label Learning with Pairwise Relevance Ordering.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Partial Multi-Label Learning with Meta Disambiguation.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Dual Active Learning for Both Model and Data Selection.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Asynchronous Active Learning with Distributed Label Querying.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Weakly Supervised Crowdsourcing Learning Based on Adversarial Consensus.
Proceedings of the International Conference on Computational Science and Computational Intelligence, 2021

Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020

Latent correlation embedded discriminative multi-modal data fusion.
Signal Process., 2020

Adaptive feature weighting for robust Lp-norm sparse representation with application to biometric image classification.
Int. J. Mach. Learn. Cybern., 2020

Incremental Multi-Label Learning with Active Queries.
J. Comput. Sci. Technol., 2020

LGSLRR: Towards fusing discriminative ordinal local and global structured low-rank representation for image recognition.
Inf. Sci., 2020

Reinforcement Learning with Supervision from Noisy Demonstrations.
CoRR, 2020

Cost-effectively Identifying Causal Effects When Only Response Variable is Observable.
Proceedings of the 37th International Conference on Machine Learning, 2020

Semi-Supervised Partial Multi-Label Learning.
Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Cross-Task and Cross-Model Active Learning with Meta Features.
Proceedings of the Big Data and Security - Second International Conference, 2020

Active Learning with Query Generation for Cost-Effective Text Classification.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Fast Multi-Instance Multi-Label Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2019

ALiPy: Active Learning in Python.
CoRR, 2019

Towards Identifying Causal Relation Between Instances and Labels.
Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

Learning Class-Conditional GANs with Active Sampling.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Multi-View Active Learning for Video Recommendation.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Self-Paced Active Learning: Query the Right Thing at the Right Time.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Active Sampling for Open-Set Classification without Initial Annotation.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Joint Estimation of Multiple Conditional Gaussian Graphical Models.
IEEE Trans. Neural Networks Learn. Syst., 2018

WoCE: A framework for Clustering Ensemble by Exploiting the Wisdom of Crowds Theory.
IEEE Trans. Cybern., 2018

Cross modal similarity learning with active queries.
Pattern Recognit., 2018

Cost-Effective Training of Deep CNNs with Active Model Adaptation.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Active Feature Acquisition with Supervised Matrix Completion.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Cost-Effective Active Learning for Hierarchical Multi-Label Classification.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Partial Multi-Label Learning.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Dual Set Multi-Label Learning.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Margin Distribution Logistic Machine.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Multi-instance multi-label active learning.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Cost-Effective Active Learning from Diverse Labelers.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016
Multi-label active learning by model guided distribution matching.
Frontiers Comput. Sci., 2016

Transfer Learning with Active Queries from Source Domain.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015
Multi-Label Active Learning: Query Type Matters.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

2014
Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning.
IEEE ACM Trans. Comput. Biol. Bioinform., 2014

Active Learning by Querying Informative and Representative Examples.
IEEE Trans. Pattern Anal. Mach. Intell., 2014

2013
Fast Multi-Instance Multi-Label Learning.
CoRR, 2013

Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

2012
Multi-instance multi-label learning.
Artif. Intell., 2012

Multi-label hypothesis reuse.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Multi-Label Learning by Exploiting Label Correlations Locally.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2008
MIML: A Framework for Learning with Ambiguous Objects
CoRR, 2008


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