Soklaski, Ryan

Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning

Mascharka, D., Tran, P., Soklaski, R., & Majumdar, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Explainable Artificial Intelligence (XAI)Multimodal Machine Learning Applications

Cited by 203

Neural scaling of deep chemical models

Frey, N. C., Soklaski, R., Axelrod, S., Samsi, S., Gómez-Bombarelli, R., Coley, C. W., & Gadepally, V. (2023) · Nature Machine Intelligence

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 125

Neural Scaling of Deep Chemical Models

Frey, N., Soklaski, R., Axelrod, S., Samsi, S., Gomez-Bombarelli, R., Coley, C., & Gadepally, V. (2022) · ChemRxiv

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 20

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models

Denison, C., MacDiarmid, M., Barez, F., Duvenaud, D., Kravec, S., Marks, S., Schiefer, N., Soklaski, R., Tamkin, A., Kaplan, J., et al. (2024) · arXiv

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 11