Posted by Qizhe Xie, Student Researcher and Thang Luong, Senior Research Scientist, Google Research, Brain Team Success in deep learning has larg...

TensorFlow Reinforcement Learning. Contribute to google-deepmind/trfl development by creating an account on GitHub.

Posted by Yinfei Yang and Amin Ahmad, Software Engineers, Google Research Since it was introduced last year, “Universal Sentence Encoder (USE) for...

BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer - guanlinchao/bert-dst

Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges. One such...

Many different deep networks have been used to approximate, accelerate or
improve traditional image operators. Among these traditional operators, many
contain parameters which need to be tweaked...

We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to...

We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could...

High-performance human language analysis tools, now with native deep learning modules in Python, available in many human languages.

Posted by Narayan Hegde, Software Engineer, Google Health and Carrie J. Cai, Research Scientist, Google Research Advances in machine learning (ML) ...

Deep learning has been shown to achieve impressive results in several domains
like computer vision and natural language processing. A key element of this
success has been the development of new...