Transformer: A Novel Neural Network Architecture for Language Understanding
Posted by Jakob Uszkoreit, Software Engineer, Natural Language Understanding Neural networks, in particular recurrent neural networks (RNNs), are now at the core of the leading approaches to language understanding tasks such as language modeling, machine translation and question answering. In “Attention Is All You Need”, we introduce the Transformer, a novel neural network architecture based on a
Federated Learning: Collaborative Machine Learning without Centralized Training Data
Posted by Brendan McMahan and Daniel Ramage, Research Scientists Standard machine learning approaches require centralizing the training data on one machine or in a datacenter. And Google has built one of the most secure and robust cloud infrastructures for processing this data to make our services better. Now for models trained from user interaction with mobile devices, we're introducing an additi
Wide & Deep Learning: Better Together with TensorFlow
Posted by Heng-Tze Cheng, Senior Software Engineer, Google Research The human brain is a sophisticated learning machine, forming rules by memorizing everyday events (“sparrows can fly” and “pigeons can fly”) and generalizing those learnings to apply to things we haven't seen before (“animals with wings can fly”). Perhaps more powerfully, memorization also allows us to further refine our generalize
DeepMind moves to TensorFlow
Posted by Koray Kavukcuoglu, Research Scientist, Google DeepMind At DeepMind, we conduct state-of-the-art research on a wide range of algorithms, from deep learning and reinforcement learning to systems neuroscience, towards the goal of building Artificial General Intelligence. A key factor in facilitating rapid progress is the software environment used for research. For nearly four years, the ope
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
KDD 2015 Best Research Paper Award: “Algorithms for Public-Private Social Networ
