Neale Ratzlaff

According to our database, Neale Ratzlaff authored at least 14 papers between 2018 and 2025.

Collaborative distances:

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

2025

Probing the Representational Power of Sparse Autoencoders in Vision Models.

Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff, Changbai Li, Phillip Howard, Vasudev Lal, Shao-Yen Tseng

CoRR, August, 2025

Semantic Specialization in MoE Appears with Scale: A Study of DeepSeek R1 Expert Specialization.

Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck, Man Luo, Sungduk Yu, Chendi Xue, Vasudev Lal

CoRR, February, 2025

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders.

Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff, Changbai Li, Phillip Howard, Vasudev Lal, Shao-Yen Tseng

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025

Training-Free Mitigation of Language Reasoning Degradation After Multimodal Instruction Tuning.

Neale Ratzlaff, Man Luo, Xin Su, Vasudev Lal, Phillip Howard

Proceedings of the 2025 AAAI Spring Symposium Series, 2025

2024

Steering Large Language Models to Evaluate and Amplify Creativity.

Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck, Shao-Yen Tseng, Vasudev Lal

CoRR, 2024

Debias your Large Multi-Modal Model at Test-Time with Non-Contrastive Visual Attribute Steering.

Neale Ratzlaff, Matthew Lyle Olson, Musashi Hinck, Estelle Aflalo, Shao-Yen Tseng, Vasudev Lal, Phillip Howard

CoRR, 2024

Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations.

Neale Ratzlaff, Matthew Lyle Olson, Musashi Hinck, Shao-Yen Tseng, Vasudev Lal, Phillip Howard

CoRR, 2024

2023

A domain-agnostic approach for characterization of lifelong learning systems.

Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha

Neural Networks, March, 2023

2021

Contrastive Identification of Covariate Shift in Image Data.

Matthew L. Olson, Thuy-Vy Nguyen, Gaurav Dixit, Neale Ratzlaff, Weng-Keen Wong, Minsuk Kahng

Proceedings of the 2021 IEEE Visualization Conference, 2021

Generative Particle Variational Inference via Estimation of Functional Gradients.

Neale Ratzlaff, Qinxun Bai, Fuxin Li, Wei Xu

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Avoiding Side Effects in Complex Environments.

Alexander Matt Turner, Neale Ratzlaff, Prasad Tadepalli

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

Implicit Generative Modeling for Efficient Exploration.

Neale Ratzlaff, Qinxun Bai, Fuxin Li, Wei Xu

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

HyperGAN: A Generative Model for Diverse, Performant Neural Networks.

Neale Ratzlaff, Fuxin Li

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks.

Neale Ratzlaff, Fuxin Li

CoRR, 2018