Research papers for computer science

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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. Columbia Engineering is committed to an open and welcoming community for all students, faculty, researchers, and staff. Click for Dean Mary Boyce’s full statement.