17 citations · 56 across the 6 of their papers we have counts for
7 papers
Proof of Response
Illia Polosukhin, Alex Skidanov
We present a mechanism that for a network of participants allows one participant of the network (Alice) to request some data from another participant (Bob) and either receive a res…
NAPS: Natural Program Synthesis Dataset
Maksym Zavershynskyi, Alex Skidanov, Illia Polosukhin
We present a program synthesis-oriented dataset consisting of human written problem statements and solutions for these problems. The problem statements were collected via crowdsour…
Neural Program Search: Solving Programming Tasks from Description and Examples
Illia Polosukhin, Alexander Skidanov
We present a Neural Program Search, an algorithm to generate programs from natural language description and a small number of input/output examples. The algorithm combines methods…
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Heng-Tze Cheng, Zakaria Haque, Lichan Hong +12
We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in o…
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar +5
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also conne…
Hierarchical Question Answering for Long Documents
Eunsol Choi, Daniel Hewlett, Alexandre Lacoste +3
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most…
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
Daniel Hewlett, Alexandre Lacoste, Llion Jones +5
We present WikiReading, a large-scale natural language understanding task and publicly-available dataset with 18 million instances. The task is to predict textual values from the s…