2 code implementations • 31 Mar 2022 • Adam Roberts, Hyung Won Chung, Anselm Levskaya, Gaurav Mishra, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves.
no code implementations • 16 Dec 2021 • Robert L. Logan IV, Alexandre Passos, Sameer Singh, Ming-Wei Chang
Textual knowledge bases such as Wikipedia require considerable effort to keep up to date and consistent.
1 code implementation • 12 Jul 2019 • Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl
In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training.
1 code implementation • 27 Feb 2019 • Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, Shanqing Cai
TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production.
no code implementations • ICLR 2018 • Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton
Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made.
no code implementations • EMNLP 2014 • Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, Andrew McCallum
There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale.
no code implementations • WS 2014 • Alexandre Passos, Vineet Kumar, Andrew McCallum
Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons.
no code implementations • ACL 2014 • Sam Anzaroot, Alexandre Passos, David Belanger, Andrew McCallum
Accurately segmenting a citation string into fields for authors, titles, etc.
no code implementations • NeurIPS 2012 • David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum
Linear chains and trees are basic building blocks in many applications of graphical models.
3 code implementations • 2 Jan 2012 • Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.