Kibok Lee

Affiliations:
  • Yonsei University, Korea
  • University of Michigan, USA (former)


According to our database1, Kibok Lee authored at least 23 papers between 2015 and 2024.

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Timeline

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Bibliography

2024
ANT: Adaptive Noise Schedule for Time Series Diffusion Models.
CoRR, 2024

Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference.
CoRR, 2024

On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Learning to Embed Time Series Patches Independently.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Soft Contrastive Learning for Time Series.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2022
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers.
CoRR, 2022

Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Improving Transferability of Representations via Augmentation-Aware Self-Supervision.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Robust Deep Learning in the Open World with Lifelong Learning and Representation Learning.
PhD thesis, 2020

i-Mix: A Strategy for Regularizing Contrastive Representation Learning.
CoRR, 2020

ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds.
CoRR, 2020

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
A Simple Randomization Technique for Generalization in Deep Reinforcement Learning.
CoRR, 2019

Robust Inference via Generative Classifiers for Handling Noisy Labels.
Proceedings of the 36th International Conference on Machine Learning, 2019

Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Incremental Learning with Unlabeled Data in the Wild.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples.
Proceedings of the 6th International Conference on Learning Representations, 2018

Hierarchical Novelty Detection for Visual Object Recognition.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017
Towards Understanding the Invertibility of Convolutional Neural Networks.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
On the Equivalence of Linear Discriminant Analysis and Least Squares.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015


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