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    Deep Learning to Learn 

    Abbeel, Pieter (Georgia Institute of Technology, 2018-08-20)
    Reinforcement learning and imitation learning have seen success in many domains, including autonomous helicopter flight, Atari, simulated locomotion, Go, robotic manipulation. However, sample complexity of these methods ...
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    Data-to-Decisions for Safe Autonomous Flight 

    Atkins, Ella (Georgia Institute of Technology, 2018-11-07)
    Traditional sensor data can be augmented with new data sources such as roadmaps and geographical information system (GIS) Lidar/video to offer emerging unmanned aircraft systems (UAS) and urban air mobility (UAM) a new ...
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    Reaching Beyond Human Accuracy With AI Datacenters 

    Diamos, Gregory (2018-10-03)
    Deep learning has enabled rapid progress in diverse problems in vision, speech, healthcare, and beyond. This progress has been driven by breakthroughs in algorithms that can harness massive datasets and powerful compute ...
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    Automated Perception in the Real World: The Problem of Scarce Data 

    Ernst, Jan (2018-11-30)
    Machine perception is a key step toward artificial intelligence in domains such as self-driving cars, industrial automation, and robotics. Much progress has been made in the past decade, driven by machine learning, ...
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    Do GANs Actually Learn the Distribution? 

    Arora, Sanjeev (Georgia Institute of Technology, 2018-02-22)
    Generative Adversarial Nets (GANs) is a framework for training deep generative models, due to Goodfellow et al'13. It involves a competition between a generator net that tries to produce realistic images, and a discriminator ...
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    Extreme scale matrix factorizations in Exploration Seismology 

    Herrmann, Felix J. (2018-04-18)
    We will present some recent work on matrix factorizations with applications that range from full-azimuth seismic data processing w/ coil acquisition to seismic data compression & recovery w/ on-the-fly data extraction, and ...
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    The Natural Language Decathlon: Multitask Learning as Question Answering 

    McCann, Bryan (2018-08-28)
    Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, ...
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    AI Information Session 

    Almejo, Anna; Bretan, Mason (2018-04-17)
    In this talk, we will cover general info about Samsung Research America and more specifically the research and projects happening within the Artificial Intelligence team including personal assistants, dialogue systems, ...
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    Pruning Deep Neural Networks with Net-Trim: Deep Learning and Compressed Sensing Meet 

    Aghasi, Alireza (2018-03-14)
    We introduce and analyze a new technique for model reduction in deep neural networks. Our algorithm prunes (sparsifies) a trained network layer-wise, removing connections at each layer by addressing a convex problem. We ...
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    Data-Driven Dialogue Systems: Models, Algorithms, Evaluation, and Ethical Challenges 

    Pineau, Joelle (Georgia Institute of Technology, 2018-02-22)
    The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. In this ...
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    AuthorAbbeel, Pieter (1)Aghasi, Alireza (1)Almejo, Anna (1)Arora, Sanjeev (1)Atkins, Ella (1)Bansal, Mohit (1)Bretan, Mason (1)Diamos, Gregory (1)Ernst, Jan (1)Gebru, Timnit (1)... View MoreSubjectMachine learning (7)Deep learning (6)Natural language decathlon (2)Reinforcement learning (2)Robotics (2)Artificial intelligence (1)Augmentation (1)Autonomy (1)Compressed sensing (1)Control (1)... View MoreDate Issued
    2018 (17)
    Has File(s)Yes (17)
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