Search Results for author: Alexandre Passos

Found 10 papers, 4 papers with code

FRUIT: Faithfully Reflecting Updated Information in Text

no code implementations16 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.

Faster Neural Network Training with Data Echoing

1 code implementation12 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.

TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning

1 code implementation27 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.

Large scale distributed neural network training through online distillation

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.

Language Modelling

Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space

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.

Word Embeddings Word Similarity

Lexicon Infused Phrase Embeddings for Named Entity Resolution

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.

Entity Resolution Learning Word Embeddings +3

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