Now showing items 1-20 of 39

    • Active Learning: From Linear Classifiers to Overparameterized Neural Networks 

      Nowak, Robert (2020-10-07)
      The field of Machine Learning (ML) has advanced considerably in recent years, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate ...
    • AI Driven Design Approach 

      Srivastava, Sanjeev (2019-04-03)
      Design Space Exploration (DSE) is an activity that is performed to systematically analyze several design points and then select the design(s) based on parameters of interest and design requirements. For complex systems, ...
    • 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, ...
    • Applying Emerging Technologies In Service of Journalism at The New York Times 

      Boonyapanachoti, Woraya (Mint); Dellaert, Frank; Essa, Irfan A.; Fleisher, Or; Kanazawa, Angjoo; Lavallee, Marc; McKeague, Mark; Porter, Lana Z. (2020-10-30)
      Emerging technologies, particularly within computer vision, photogrammetry, and spatial computing, are unlocking new forms of storytelling for journalists to help people understand the world around them. In this talk, ...
    • 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 ...
    • 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, ...
    • 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 ...
    • The Data-Driven Analysis of Literature 

      Bamman, David (2019-11-15)
      Literary novels push the limits of natural language processing. While much work in NLP has been heavily optimized toward the narrow domains of news and Wikipedia, literary novels are an entirely different animal--the long, ...
    • 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 ...
    • 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 ...
    • 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 ...
    • Deep Networks for Pixel Level Inference with Applications to Medical Imaging 

      Chopra, Sumit (Georgia Institute of Technology, 2017-09-26)
    • A Discussion on Fairness in Machine Learning with Georgia Tech Faculty 

      Cummings, Rachel; Desai, Devan; Gupta, Swati; Hoffman, Judy (2019-11-06)
      Fairness in machine learning and artificial intelligence is a hot, and important topic in tech today. Join Georgia Tech faculty members Judy Hoffman, Rachel Cummings, Deven Desai, and Swati Gupta for a panel discussion on ...
    • 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 ...
    • 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 ...
    • 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 ...
    • The Geometry of Community Detection via the MMSE Matrix 

      Reeves, Galen (2019-09-04)
      The information-theoretic limits of community detection have been studied extensively for network models with high levels of symmetry or homogeneity. In this talk, Reeves will present a new approach that applies to a broader ...
    • Global Optimality Guarantees for Policy Gradient Methods 

      Russo, Daniel (2020-03-11)
      Policy gradients methods are perhaps the most widely used class of reinforcement learning algorithms. These methods apply to complex, poorly understood, control problems by performing stochastic gradient descent over a ...
    • Learning to Optimize from Data: Faster, Better, and Guaranteed 

      Wang, Zhangyang (2019-11-20)
      Learning and optimization are closely related: state-of-the-art learning problems hinge on the sophisticated design of optimizers. On the other hand, the optimization cannot be considered as independent from data, since ...
    • 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 ...