Now showing items 1-20 of 26

    • 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, ...
    • 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 ...
    • 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)
    • 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 ...
    • 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 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, ...
    • The New Machine Leaming Center at GA Tech: Plans end Aspirations 

      Essa, Irfan (Georgia Institute of Technology, 2017-03-01)
      The Interdisciplinary Research Center (IRC) for Machine Learning at Georgia Tech (ML@GT) was established in Summer 2016 to foster research and academic activities in and around the discipline of Machine Learning. This ...
    • An overview of deep learning frameworks and an introduction to PyTorch 

      Chintala, Soumith (2017-09-06)
      In this talk, you will get an exposure to the various types of deep learning frameworks – declarative and imperative frameworks such as TensorFlow and PyTorch. After a broad overview of frameworks, you will be introduced ...
    • Perception at Magic Leap 

      Swaminathan, Ashwin (2019-04-19)
      This talk presents the importance of Computer Vision and Deep learning techniques in making Magic Leap an effective spatial computing platform. The four fundamental modalities are introduced: head pose tracking, world ...
    • 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 ...
    • 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 ...
    • 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 ...