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pro vyhledávání: '"Rao, Vinay P"'
Autor:
Balu, Aditya, Botelho, Sergio, Khara, Biswajit, Rao, Vinay, Hegde, Chinmay, Sarkar, Soumik, Adavani, Santi, Krishnamurthy, Adarsh, Ganapathysubramanian, Baskar
We consider the distributed training of large-scale neural networks that serve as PDE solvers producing full field outputs. We specifically consider neural solvers for the generalized 3D Poisson equation over megavoxel domains. A scalable framework i
Externí odkaz:
http://arxiv.org/abs/2104.14538
Relation extraction is used to populate knowledge bases that are important to many applications. Prior datasets used to train relation extraction models either suffer from noisy labels due to distant supervision, are limited to certain domains or are
Externí odkaz:
http://arxiv.org/abs/2102.09681
Autor:
Rao, Vinay, Sohl-Dickstein, Jascha
We perform an extensive empirical study of the statistical properties of Batch Norm and other common normalizers. This includes an examination of the correlation between representations of minibatches, gradient norms, and Hessian spectra both at init
Externí odkaz:
http://arxiv.org/abs/2010.10687
Publikováno v:
The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '19), August 4--8, 2019, Anchorage, AK, USA
We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduc
Externí odkaz:
http://arxiv.org/abs/1905.13322
We develop a mean field theory for batch normalization in fully-connected feedforward neural networks. In so doing, we provide a precise characterization of signal propagation and gradient backpropagation in wide batch-normalized networks at initiali
Externí odkaz:
http://arxiv.org/abs/1902.08129
Akademický článek
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Akademický článek
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Autor:
Battenberg, Eric, Child, Rewon, Coates, Adam, Fougner, Christopher, Gaur, Yashesh, Huang, Jiaji, Jun, Heewoo, Kannan, Ajay, Kliegl, Markus, Kumar, Atul, Liu, Hairong, Rao, Vinay, Satheesh, Sanjeev, Seetapun, David, Sriram, Anuroop, Zhu, Zhenyao
Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition. However, the causality and latency constraints of production systems put end-to-end speech models
Externí odkaz:
http://arxiv.org/abs/1705.04400
Publikováno v:
Journal of Clinical Oncology; 2024 Supplement 10, Vol. 20, p336-336, 19p
In training speech recognition systems, labeling audio clips can be expensive, and not all data is equally valuable. Active learning aims to label only the most informative samples to reduce cost. For speech recognition, confidence scores and other l
Externí odkaz:
http://arxiv.org/abs/1612.03226