Machine Learning at Georgia Institute of Technology (ML@GT) is an interdisciplinary research center that will serve as a home to education and research around ML and related fields.

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Recent Submissions

  • Situated Natural Language Understanding 

    Misra, Dipendra (2019-03-08)
  • Contextual AI - The Next Frontier Towards Human-Centric Artificial Intelligence 

    Brdiczka, Oliver (2019-02-28)
    This talk motivates a more human-centric wave of AI, dubbed Contextual AI. Contextual AI does not refer to a specific algorithm or machine learning method - instead, it takes a human-centric view and approach to AI. The ...
  • 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, ...
  • 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 ...
  • Multimodal, Personable, and Knowledgeable Language Generation 

    Bansal, Mohit (2018-11-19)
    In this talk, I will discuss my group's recent work on state-of-the-art natural language generation (NLG) and dialogue models that are multimodal, personality-based, and knowledge-rich. First, we will discuss dialogue ...
  • The Statistical Foundations of Learning to Control 

    Recht, Benjamin (2018-11-14)
    Given the dramatic successes in machine learning and reinforcement learning over the past half decade, there has been a surge of interest in applying these techniques to continuous control problems in robotics and autonomous ...
  • Practical Applications of Signal Processing and Machine Learning in a Dynamic Retail Environment 

    Poliner, Graham (2018-10-31)
    The retail industry is the midst of rapid change due to intensifying competition from fragmented and non-traditional sources, expansion of assortment breadth and product availability, and more transparent pricing. Evolving ...
  • Few-shot Learning with Meta-Learning: Progress Made and Challenges Ahead 

    Larochelle, Hugo (2018-10-15)
    A lot of the recent progress on many AI tasks enabled in part by the availability of large quantities of labeled data. Yet, humans are able to learn concepts from as little as a handful of examples. Meta-learning is a very ...
  • 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 ...
  • Understanding the limitations of AI: When Algorithms Fail 

    Gebru, Timnit (2018-09-05)
    Automated decision-making tools are currently used in high stakes scenarios. From natural language processing tools used to automatically determine one’s suitability for a job, to health diagnostic systems trained to ...
  • 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, ...
  • 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 ...
  • 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 ...
  • 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, ...
  • Asynchronous (Sub)gradient-Push 

    Rabbat, Mike (2018-04-04)
    We consider a multi-agent framework for distributed optimization where each agent in the network has access to a local convex function and the collective goal is to achieve consensus on the parameters that minimize the sum ...
  • The Science of Autonomy: A "Happy" Symbiosis Among Control, Learning and Physics 

    Theodorou, Evangelos A. (2018-03-28)
    In this talk I will present an information theoretic approach to stochastic optimal control and inference that has advantages over classical methodologies and theories for decision making under uncertainty. The main idea ...
  • 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 ...
  • 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 ...
  • 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 ...
  • Deep Networks for Pixel Level Inference with Applications to Medical Imaging 

    Chopra, Sumit (Georgia Institute of Technology, 2017-09-26)

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