Identifying and interpreting tuning dimensions in deep networks. - Nolan S. Dey; J. Eric Taylor; Bryan P. Tripp; Alexander Wong; Graham W. Taylor (CoRR, 2020)

37, 000 Human-Planned Robotic Grasps With Six Degrees of Freedom. - Victor Reyes Osorio; Rajan Iyengar; Xueyang Yao; Presish Bhattachan; Adrian Ragobar; Nolan Dey; Bryan P. Tripp (IEEE Robotics Autom. Lett., 2020)

Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster. - Nolan Dey; Gurpreet Gosal; Zhiming Chen; Hemant Khachane; William Marshall; Ribhu Pathria; Marvin Tom; Joel Hestness (CoRR, 2023)

BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model. - Nolan Dey; Daria Soboleva; Faisal Al-Khateeb; Bowen Yang; Ribhu Pathria; Hemant Khachane; Shaheer Muhammad; Zhiming Chen; Robert Myers; Jacob Robert Steeves; Natalia Vassilieva; Marvin Tom; et al. (CoRR, 2023)

Position Interpolation Improves ALiBi Extrapolation. - Faisal Al-Khateeb; Nolan Dey; Daria Soboleva; Joel Hestness (CoRR, 2023)

Sparse maximal update parameterization: A holistic approach to sparse training dynamics. - Nolan Dey; Shane Bergsma; Joel Hestness (NeurIPS, 2024)

Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for LLMs. - Shane Bergsma; Nolan Simran Dey; Gurpreet Gosal; Gavia Gray; Daria Soboleva; Joel Hestness (ICLR, 2025)

Neuron-based explanations of neural networks sacrifice completeness and interpretability. - Nolan Simran Dey; J. Eric Taylor; Alexander Wong; Bryan P. Tripp; Graham W. Taylor (Trans. Mach. Learn. Res., 2025)

Don't be lazy: CompleteP enables compute-efficient deep transformers. - Nolan Dey; Bin Claire Zhang; Lorenzo Noci; Mufan Bill Li; Blake Bordelon; Shane Bergsma; Cengiz Pehlevan; Boris Hanin; Joel Hestness (CoRR, 2025)

Power Lines: Scaling Laws for Weight Decay and Batch Size in LLM Pre-training. - Shane Bergsma; Nolan Dey; Gurpreet Gosal; Gavia Gray; Daria Soboleva; Joel Hestness (CoRR, 2025)