Recent research papers
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Enter the characters you see below Sorry, we just need to make sure you’re not a robot. If you are the account owner, please submit ticket for further information. We work on computer science problems that define the technology of today and tomorrow. We identify a fundamental source of error in Q-learning and other forms of dynamic programming with function approximation. Delusional bias arises when the approximation architecture limits the class of expressible greedy policies. A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatiotemporal representations.
Robotic learning algorithms based on reinforcement, self-supervision, and imitation can acquire end-to-end controllers from raw sensory inputs such as images. These end-to-end controllers acquire perception systems that are tailored to the task, picking up on the cues that are most useful for the task at hand. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. We introduce Fluid Annotation, an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field. Our researchers publish regularly in academic journals, release projects as open source, and apply research to Google products. Researchers across Google are innovating across many domains. We challenge conventions and reimagine technology so that everyone can benefit.
Distill article exploring how feature visualization can combine together with other interpretability techniques to understand aspects of how networks make decisions. Assessing this risk is critical first step toward reducing the likelihood that a patient suffers a CV event in the future. Learn more about PAIR, an initiative using human-centered research and design to make AI partnerships productive, enjoyable, and fair. We generate human-like speech from text using neural networks trained using only speech examples and corresponding text transcripts.
With motion photos, a new camera feature available on the Pixel 2 and Pixel 2 XL phones, you no longer have to choose between a photo and a video so every photo you take captures more of the moment. Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. Get to know Magenta, a research project exploring the role of machine learning in the process of creating art and music. Our teams advance the state of the art through research, systems engineering, and collaboration across Google. Our global reach means that research teams across the company tackle tough problems together. Google’s mission presents many exciting algorithmic and optimization challenges across different product recent research papers including Search, Ads, Social, and Google Infrastructure.
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Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. A major research effort involves the management of structured data within the enterprise. The goal is to discover, index, monitor, and organize this type of data in order to make it easier to access high-quality datasets. This type of data carries different, and often richer, semantics than structured data on the Web, which in turn raises new opportunities and technical challenges in their management. Furthermore, Data Management research across Google allows us to build technologies that power Google’s largest businesses through scalable, reliable, fast, and general-purpose infrastructure for large-scale data processing as a service. The proliferation of machine learning means that learned classifiers lie at the core of many products across Google.
However, questions in practice are rarely so clean as to just to use an out-of-the-box algorithm. Data mining lies at the heart of many of these questions, and the research done at Google is at the forefront of the field. Whether it is finding more efficient algorithms for working with massive data sets, developing privacy-preserving methods for classification, or designing new machine learning approaches, our group continues to push the boundary of what is possible. No matter how powerful individual computers become, there are still reasons to harness the power of multiple computational units, often spread across large geographic areas.
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Capital case study problems may be classified as primary markets and secondary markets.
According to Azarian, documentation is case study database to track all aspects of the application from development to maintenance to knowledge transfer.