Now showing items 1-20 of 25

    • 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 ...
    • Sum-Product Networks: The Next Generation of Deep Models 

      Domingos, Pedro (2017-04-19)
      The two main types of deep learning are function approximation and probability estimation. Function approximators like convolutional neural networks are robust and allow for real-time inference, but are very inflexible, ...
    • 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 ...
    • TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow 

      Silberman, Nathan (Georgia Institute of Technology, 2017-09-07)
      TF-Slim is a TensorFlow-based library with various components. These include modules for easily defining neural network models with few lines of code, routines for training and evaluating such models in a highly distributed ...
    • Deep Networks for Pixel Level Inference with Applications to Medical Imaging 

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