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PAIRED: A New Multi-agent Approach for Adversarial Environment Generation

Posted by Natasha Jaques, Google Research and Michael Dennis, UC Berkeley The effectiveness of any machine learning method is critically dependent on its training data. In the case of reinforcement learning (RL), one can rely either on limited data collected by an agent interacting with the real world, or a simulated training environment that can be...

Lyra: A New Very Low-Bitrate Codec for Speech Compression

Posted by Alejandro Luebs, Software Engineer and Jamieson Brettle, Product Manager, Chrome Connecting to others online via voice and video calls is something that is increasingly a part of everyday life. The real-time communication frameworks, like WebRTC, that make this possible depend on efficient compression techniques, codecs, to encode (or decode)...

The Technology Behind Cinematic Photos

Posted by Per Karlsson and Lucy Yu, Software Engineers, Google Research Looking at photos from the past can help people relive some of their most treasured moments. Last December we launched Cinematic photos, a new feature in Google Photos that aims to recapture the sense of immersion felt the moment a photo was taken, simulating camera motion and parallax...

Introducing Model Search: An Open Source Platform for Finding Optimal ML Models

Posted by Hanna Mazzawi, Research Engineer and Xavi Gonzalvo, Research Scientist, Google Research The success of a neural network (NN) often depends on how well it can generalize to various tasks. However, designing NNs that can generalize well is challenging because the research community's understanding of how a neural network generalizes is currently...

Mastering Atari with Discrete World Models

Posted by Danijar Hafner, Student Researcher, Google Research Deep reinforcement learning (RL) enables artificial agents to improve their decisions over time. Traditional model-free approaches learn which of the actions are successful in different situations by interacting with the environment through a large amount of trial and error. In contrast,...

Rearranging the Visual World

Posted by Andy Zeng and Pete Florence, Research Scientists, Robotics at Google Rearranging objects (such as organizing books on a bookshelf, moving utensils on a dinner table, or pushing piles of coffee beans) is a fundamental skill that can enable robots to physically interact with our diverse and unstructured world. While easy for people, accomplishing...

3D Scene Understanding with TensorFlow 3D

Posted by Alireza Fathi, Research Scientist and Rui Huang, AI Resident, Google Research The growing ubiquity of 3D sensors (e.g., Lidar, depth sensing cameras and radar) over the last few years has created a need for scene understanding technology that can process the data these devices capture. Such technology can enable machine learning (ML) systems...

Uncovering Unknown Unknowns in Machine Learning

Posted by Lora Aroyo and Praveen Paritosh, Research Scientists, Google Research The performance of machine learning (ML) models depends both on the learning algorithms, as well as the data used for training and evaluation. The role of the algorithms is well studied and the focus of a multitude of challenges, such as SQuAD, GLUE, ImageNet, and many others....

TracIn — A Simple Method to Estimate Training Data Influence

Posted by Frederick Liu and Garima Pruthi, Software Engineers, Google Research The quality of a machine learning (ML) model’s training data can have a significant impact on its performance. One measure of data quality is the notion of influence, i.e., the degree to which a given training example affects the model and its predictive performance. And...

Machine Learning for Computer Architecture

Posted by Amir Yazdanbakhsh, Research Scientist, Google Research One of the key contributors to recent machine learning (ML) advancements is the development of custom accelerators, such as Google TPUs and Edge TPUs, which significantly increase available compute power unlocking various capabilities such as AlphaGo, RankBrain, WaveNets, and Conversational...

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