Kevin J. Liang

According to our database, Kevin J. Liang authored at least 20 papers between 2018 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

Few-shot Learning with Noisy Labels.

Kevin J. Liang, Samrudhdhi B. Rangrej, Vladan Petrovic, Tal Hassner

CoRR, 2022

Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection.

Li Yin, Juan M. Perez-Rua, Kevin J. Liang

CoRR, 2022

Extending One-Stage Detection with Open-World Proposals.

Sachin Konan, Kevin J. Liang, Li Yin

CoRR, 2022

WAFFLe: Weight Anonymized Factorization for Federated Learning.

Weituo Hao, Nikhil Mehta, Kevin J. Liang, Pengyu Cheng, Mostafa El-Khamy, Lawrence Carin

IEEE Access, 2022

2021

Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning.

Vinay Kumar Verma, Kevin J. Liang, Nikhil Mehta, Lawrence Carin

CoRR, 2021

MixKD: Towards Efficient Distillation of Large-scale Language Models.

Kevin J. Liang, Weituo Hao, Dinghan Shen, Yufan Zhou, Weizhu Chen, Changyou Chen, Lawrence Carin

Proceedings of the 9th International Conference on Learning Representations, 2021

Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?

Nathan Inkawhich, Kevin J. Liang, Jingyang Zhang, Huanrui Yang, Hai Li, Yiran Chen

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Efficient Feature Transformations for Discriminative and Generative Continual Learning.

Vinay Kumar Verma, Kevin J. Liang, Nikhil Mehta, Piyush Rai, Lawrence Carin

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Towards Fair Federated Learning With Zero-Shot Data Augmentation.

Weituo Hao, Mostafa El-Khamy, Jungwon Lee, Jianyi Zhang, Kevin J. Liang, Changyou Chen, Lawrence Carin

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

A Multiplexed Network for End-to-End, Multilingual OCR.

Jing Huang, Guan Pang, Rama Kovvuri, Mandy Toh, Kevin J. Liang, Praveen Krishnan, Xi Yin, Tal Hassner

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors.

Nikhil Mehta, Kevin J. Liang, Vinay Kumar Verma, Lawrence Carin

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Deep Automatic Threat Recognition: Considerations for Airport X-Ray Baggage Screening.

Kevin J. Liang

PhD thesis, 2020

Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images.

John B. Sigman, Gregory P. Spell, Kevin J. Liang, Lawrence Carin

CoRR, 2020

Bayesian Nonparametric Weight Factorization for Continual Learning.

Nikhil Mehta, Kevin J. Liang, Lawrence Carin

CoRR, 2020

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability.

Nathan Inkawhich, Kevin J. Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, Yiran Chen

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Transferable Perturbations of Deep Feature Distributions.

Nathan Inkawhich, Kevin J. Liang, Lawrence Carin, Yiran Chen

Proceedings of the 8th International Conference on Learning Representations, 2020

Object Detection as a Positive-Unlabeled Problem.

Yuewei Yang, Kevin J. Liang, Lawrence Carin

Proceedings of the 31st British Machine Vision Conference 2020, 2020

2019

Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection.

Kevin J. Liang, John B. Sigman, Gregory P. Spell, Dan Strellis, William Chang, Felix Liu, Tejas Mehta, Lawrence Carin

CoRR, 2019

Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods.

Kevin J. Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Generative Adversarial Network Training is a Continual Learning Problem.

Kevin J. Liang, Chunyuan Li, Guoyin Wang, Lawrence Carin

CoRR, 2018