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

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A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level von 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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Learning hierarchical similarity metrics von Verma, Nakul, Mahajan, Dhruv, Sellamanickam, Sundararajan, Nair, Vinod

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Solving Probability and Statistics Problems by Probabilistic Program Synthesis at Human Level and Predicting Solvability von Tang, Leonard, Ke, Elizabeth, Singh, Nikhil, Feng, Bo, Austin, Derek, Verma, Nakul, Drori, Iddo

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Stochastic Neighbor Embedding under f-divergences von Im, Daniel Jiwoong, Verma, Nakul, Branson, Kristin

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

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