Search Results - Verma, Nakul

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  1. 1
    Cortical pattern generation during dexterous movement is input-driven

    Cortical pattern generation during dexterous movement is input-driven by Sauerbrei, Britton A., Guo, Jian-Zhong, Cohen, Jeremy D., Mischiati, Matteo, Guo, Wendy, Kabra, Mayank, Verma, Nakul, Mensh, Brett, Branson, Kristin, Hantman, Adam W.

    Published in Nature (London)
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  2. 2
    A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level

    A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level by Drori, Iddo, Zhang, Sarah, Shuttleworth, Reece, Tang, Leonard, Lu, Albert, Ke, Elizabeth, Liu, Kevin, Chen, Linda, Tran, Sunny, Cheng, Newman, Wang, Roman, Singh, Nikhil, Patti, Taylor L., Lynch, Jayson, Shporer, Avi, Verma, Nakul, Wu, Eugene, Strang, Gilbert

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  3. 3
    Learning hierarchical similarity metrics

    Learning hierarchical similarity metrics by Verma, Nakul, Mahajan, Dhruv, Sellamanickam, Sundararajan, Nair, Vinod

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  4. 4
    Automated Symbolic Law Discovery: A Computer Vision Approach

    Automated Symbolic Law Discovery: A Computer Vision Approach by Xing, Hengrui, Salleb-Aouissi, Ansaf, Verma, Nakul

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  5. 5
    Improving Model Training via Self-learned Label Representations

    Improving Model Training via Self-learned Label Representations by Yu, Xiao, Verma, Nakul

    Published in arXiv.org
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  6. 6
    Solving Linear Algebra by Program Synthesis

    Solving Linear Algebra by Program Synthesis by Drori, Iddo, Verma, Nakul

    Published in arXiv.org
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  7. 7
    Contrastive Loss is All You Need to Recover Analogies as Parallel Lines

    Contrastive Loss is All You Need to Recover Analogies as Parallel Lines by Narutatsu Ri, Lee, Fei-Tzin, Verma, Nakul

    Published in arXiv.org
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  8. 8
    Meta-Learning to Cluster

    Meta-Learning to Cluster by Jiang, Yibo, Verma, Nakul

    Published in arXiv.org
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  9. 9
    Using Deep Autoregressive Models as Causal Inference Engines

    Using Deep Autoregressive Models as Causal Inference Engines by Im, Daniel Jiwoong, Zhang, Kevin, Verma, Nakul, Cho, Kyunghyun

    Published in arXiv.org
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  10. 10
    Metric Learning on Manifolds

    Metric Learning on Manifolds by Aalto, Max, Verma, Nakul

    Published in arXiv.org
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  11. 11
    A Dataset for Learning University STEM Courses at Scale and Generating Questions at a Human Level

    A Dataset for Learning University STEM Courses at Scale and Generating Questions at a Human Level by Drori, Iddo, Zhang, Sarah, Chin, Zad, Shuttleworth, Reece, Lu, Albert, Chen, Linda, Birbo, Bereket, He, Michele, Lantigua, Pedro, Tran, Sunny, Hunter, Gregory, Feng, Bo, Cheng, Newman, Wang, Roman, Hicke, Yann, Surbehera, Saisamrit, Raghavan, Arvind, Siemenn, Alexander, Singh, Nikhil, Lynch, Jayson, Shporer, Avi, Verma, Nakul, Buonassisi, Tonio, Solar-Lezama, Armando

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  12. 12
    A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level

    A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level by Drori, Iddo, Zhang, Sarah, Shuttleworth, Reece, Tang, Leonard, Lu, Albert, Ke, Elizabeth, Liu, Kevin, Chen, Linda, Tran, Sunny, Newman, Cheng, Wang, Roman, Singh, Nikhil, Patti, Taylor L, Lynch, Jayson, Shporer, Avi, Verma, Nakul, Wu, Eugene, Strang, Gilbert

    Published in arXiv.org
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  13. 13
    An analysis of document graph construction methods for AMR summarization

    An analysis of document graph construction methods for AMR summarization by Lee, Fei-Tzin, Kedzie, Chris, Verma, Nakul, McKeown, Kathleen

    Published in arXiv.org
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  14. 14
    Solving Probability and Statistics Problems by Program Synthesis

    Solving Probability and Statistics Problems by Program Synthesis by Tang, Leonard, Ke, Elizabeth, Singh, Nikhil, Verma, Nakul, Drori, Iddo

    Published in arXiv.org
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  15. 15
    Model-Agnostic Meta-Learning using Runge-Kutta Methods

    Model-Agnostic Meta-Learning using Runge-Kutta Methods by Im, Daniel Jiwoong, Jiang, Yibo, Verma, Nakul

    Published in arXiv.org
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  16. 16
    Sample complexity of learning Mahalanobis distance metrics

    Sample complexity of learning Mahalanobis distance metrics by Verma, Nakul, Branson, Kristin

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  17. 17
    Noise-tolerant fair classification

    Noise-tolerant fair classification by Lamy, Alexandre Louis, Zhong, Ziyuan, Menon, Aditya Krishna, Verma, Nakul

    Published in arXiv.org
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  18. 18
    Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics

    Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics by Cowgill, Bo, Dell'Acqua, Fabrizio, Deng, Samuel, Hsu, Daniel, Verma, Nakul, Chaintreau, Augustin

    Published in arXiv.org
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  19. 19
    Latent variables based data estimation for sensing applications

    Latent variables based data estimation for sensing applications by Verma, N., Zappi, P., Rosing, T.

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  20. 20
    A concentration theorem for projections

    A concentration theorem for projections by Dasgupta, Sanjoy, Hsu, Daniel, Verma, Nakul

    Published in arXiv.org
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