Gruver, Nate

1. Large Language Models Are Zero-Shot Time Series Forecasters
   2023/10/11 by Nate Gruver, Marc Finzi, Gruver, Nate +5 · 3 voices · 87 citations
   Computer Science · #Topic Modeling #Natural Language Processing Techniques #Advanced Text Analysis Techniques
2. On Feature Learning in the Presence of Spurious Correlations
   2022/10/20 by Pavel Izmailov, Polina Kirichenko, Izmailov, Pavel +5 · 21 citations
   Engineering · Computer Science · #Industrial Vision Systems and Defect Detection #Face and Expression Recognition #Image Enhancement Techniques
3. Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
   2024/02/06 by Gruver, Nate, Sriram, Anuroop, Madotto, Andrea +3 · 22 citations
   #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci)
4. Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
   2022/03/23 by Stanton, Samuel, Maddox, Wesley, Gruver, Nate +4 · 14 citations
   #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #Quantitative Methods (q-bio.QM)
5. Large Language Models Must Be Taught to Know What They Don't Know
   2024/06/12 by Kapoor, Sanyam, Gruver, Nate, Roberts, Manley +7 · 20 citations
   #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
6. The Lie Derivative for Measuring Learned Equivariance
   2022/10/06 by Nate Gruver, Gruver, Nate, Marc Finzi +5 · 6 citations
   Computer Science · Materials Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Model Reduction and Neural Networks #Neural Networks and Applications
7. Adaptive Informative Path Planning with Multimodal Sensing
   2020/03/21 by Choudhury, Shushman, Gruver, Nate, Kochenderfer, Mykel J. · 1 citation
   #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences
8. Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
   2024/12/10 by Alan Nawzad Amin, Nate Gruver, Amin, Alan Nawzad +15 · 2 voices · 3 citations
   #stat.ML #cs.LG #q-bio.BM
9. Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion
   2025/06/10 by Amin, Alan N., Gruver, Nate, Wilson, Andrew Gordon · 6 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
10. Deconstructing the Inductive Biases of Hamiltonian Neural Networks
    2022/02/10 by Gruver, Nate, Finzi, Marc, Stanton, Samuel +1 · 1 citation
    #Data Analysis #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics and Probability (physics.data-an)
11. Triangle Multiplication Is All You Need For Biomolecular Structure Representations
    2025/10/21 by Jeffrey Ouyang-Zhang, P. Thirusakthi Murugan, Ouyang-Zhang, Jeffrey +17 · 2 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · #Protein Structure and Dynamics #Computational Drug Discovery Methods #Machine Learning in Bioinformatics
12. Pearl: A Foundation Model for Placing Every Atom in the Right Location
    2025/10/28 by Genesis Research Team, Dobles, Alejandro, Jovic, Nina +37 · 1 citation
    #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM)