Keith G. Mills

According to our database, Keith G. Mills authored at least 22 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

2D Pre-Training for 3D Pose Estimation.

Liyao Jiang, Ruichen Chen, Keith G. Mills

CoRR, April, 2026

Naïve PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation.

Joong Ho Kim, Nicholas Thai, Souhardya Saha Dip, Dong Lao, Keith G. Mills

CoRR, March, 2026

2025

Charting the Future of Scholarly Knowledge with AI: A Community Perspective.

Azanzi Jiomekong, Hande Küçük-McGinty, Keith G. Mills, Allard Oelen, Enayat Rajabi, Harry McElroy, Antrea Christou, Anmol Saini, Janice Anta Zebaze, Hannah Kim, Anna Jacyszyn, Sören Auer

CoRR, September, 2025

Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence.

Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, Di Niu

CoRR, May, 2025

Applying Graph Explanation to Operator Fusion.

Keith G. Mills, Muhammad Fetrat Qharabagh, Weichen Qiu, Fred X. Han, Mohammad Salameh, Wei Lu, Shangling Jui, Di Niu

CoRR, January, 2025

FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers.

Ruichen Chen, Keith G. Mills, Di Niu

Trans. Mach. Learn. Res., 2025

Qua2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models.

Keith G. Mills, Mohammad Salameh, Ruichen Chen, Negar Hassanpour, Wei Lu, Di Niu

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

Qua²SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models.

Keith G. Mills, Mohammad Salameh, Ruichen Chen, Negar Hassanpour, Wei Lu, Di Niu

CoRR, 2024

EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time.

Shengyao Lu, Bang Liu, Keith G. Mills, Jiao He, Di Niu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

GOAt: Explaining Graph Neural Networks via Graph Output Attribution.

Shengyao Lu, Keith G. Mills, Jiao He, Bang Liu, Di Niu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Building Optimal Neural Architectures Using Interpretable Knowledge.

Keith G. Mills, Fred X. Han, Mohammad Salameh, Shengyao Lu, Chunhua Zhou, Jiao He, Fengyu Sun, Di Niu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

A General-Purpose Transferable Predictor for Neural Architecture Search.

Fred X. Han, Keith G. Mills, Fabian Chudak, Parsa Riahi, Mohammad Salameh, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

AutoGO: Automated Computation Graph Optimization for Neural Network Evolution.

Mohammad Salameh, Keith G. Mills, Negar Hassanpour, Fred X. Han, Shuting Zhang, Wei Lu, Shangling Jui, Chunhua Zhou, Fengyu Sun, Di Niu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

AIO-P: Expanding Neural Performance Predictors beyond Image Classification.

Keith G. Mills, Di Niu, Mohammad Salameh, Weichen Qiu, Fred X. Han, Puyuan Liu, Jialin Zhang, Wei Lu, Shangling Jui

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

GENNAPE: Towards Generalized Neural Architecture Performance Estimators.

Keith G. Mills, Fred X. Han, Jialin Zhang, Fabian Chudak, Ali Safari Mamaghani, Mohammad Salameh, Wei Lu, Shangling Jui, Di Niu

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning.

Shengyao Lu, Bang Liu, Keith G. Mills, Shangling Jui, Di Niu

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning.

Keith G. Mills, Fred X. Han, Mohammad Salameh, Seyed Saeed Changiz Rezaei, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu

CoRR, 2021

Exploring Neural Architecture Search Space via Deep Deterministic Sampling.

Keith G. Mills, Mohammad Salameh, Di Niu, Fred X. Han, Seyed Saeed Changiz Rezaei, Hengshuai Yao, Wei Lu, Shuo Lian, Shangling Jui

IEEE Access, 2021

Generative Adversarial Neural Architecture Search.

Seyed Saeed Changiz Rezaei, Fred X. Han, Di Niu, Mohammad Salameh, Keith G. Mills, Shuo Lian, Wei Lu, Shangling Jui

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search.

Keith G. Mills, Fred X. Han, Jialin Zhang, Seyed Saeed Changiz Rezaei, Fabian Chudak, Wei Lu, Shuo Lian, Shangling Jui, Di Niu

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

L2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning.

Keith G. Mills, Fred X. Han, Mohammad Salameh, Seyed Saeed Changiz Rezaei, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2019

Android Malware Detection Based on Factorization Machine.

Chenglin Li, Keith G. Mills, Di Niu, Rui Zhu, Hongwen Zhang, Husam Kinawi

IEEE Access, 2019