In this session, data scientists from CellStrat AI Lab will present demos and presentations on cutting-edge AI solutions in :-

  • Computer Vision - Image Segmentation with FCN/UNets/DeepLab/ESPNet, Image Processing, Pose Estimation with DensePose
  • Natural Language Processing (NLP) - Latest NLP and Text Analytics with BERT, NER, Neural Language Translation etc to solve problems such as text summarization, QnA systems, video captioning etc.
  • Reinforcement Learning (RL) - Train Atari Video Games with RL, Augmented Random Search, Deep Q Learning etc. Apply RL techniques for gaming, financial portfolios, driverless cars etc. Train Robots with MuJoCo simulator.
  • Driverless Cars - Demo on multi-class roads datasets, path planning and navigation control for cars etc.
  • Neural Network Architectures - Faster and Smaller Neural Networks with MorphNet
 
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Outline/Structure of the Workshop

Advanced Vision presentations + demos

  • Insight analysis on radiological images of cancer cells
    • Advanced Image Segmentation techniques such as FCN, U-Net, DeepLab and ESPNet are being used to detect anomalous cells which indicate onset or risk of cancer or other abnormalities in X-Rays or CT-Scans.
  • Extracting human pose from a motion video for AR/VR or advertising videos
    • Detection of human pose as a separate artifact in motion videos allows one to create rich and intuitive overlays for purposes of advertising, cinematography or augmented reality / virtual reality applications
  • Photo-realistic Style Transfer
    • During classic Neural Style Transfer protocol, one combines style from one image and content from another image. However in this case, the output exhibits distortion and looks like it has been painted. Photo-realistic Style Transfer, on the other hand, adds photo-realism to the art of Style Transfer, thereby rendering highly realistic and original-like images. It achieves this by adding a constraint on the input to the output transformation to be locally affine in the colorspace. This approach successfully suppresses distortion while retaining original image elements of time of day, weather, season or artistic edits.

Advanced NLP presentations + demos

  • Text Summarization for Legal / Pharmaceutical literature
    • Long form literature often requires summarization for easy consumption and analysis. Text Summarization follows various techniques of extractive or abstractive text summarization to find intelligent summaries which correctly represent the original text interpretation.
  • Developing a Question Answer system using BERT API
    • Shortage of training data can hobble NLP development. Google has published the BERT (Bidirectional Encoder Representations from Transformers) API along with several pre-trained BERT models which leverage global publicly available text data (e.g. Wikipedia). BERT exposes intuitive language representations using unsupervised learning techniques and renders capable applications such as sentiment analysis, Question and Answer system, Named Entity Recognition, sentence classification etc.
  • Automatic Video Captioning
    • Automatic video commentary is a very useful application for motion videos and can be useful in scene description, automatic captioning in TV or movies, sports commentary and many other video applications. We demonstrate a powerful video captioning application which uses a series of algorithms to accurately generate captions for a new video.

Advanced RL presentation + demos

  • Robotic Arm simulation for a manufacturing assembly line process
    • Robotic automation for manufacturing assembly lines is a very powerful productivity tool for businesses of different types. We demonstrate use of Reinforcement Learning to train a Robotic Arm using a MuJoCo simulator, a movement control environment.

Driverless Car presentations + demos

  • Training a driverless car to visualize the road ahead for decisioning control
    • We will demonstrate advanced image segmentation with ESPNet algorithm which can help visualize the driving scenarios accurately, so as to allow the autonomous system to take appropriate action considering the obstacle views.
  • Training a Racing Car with Rewards-based Learning
    • We demonstrate how to use RL to train a racing car for path planning and navigation control.

Neural Network Architecture

  • Developing efficient neural networks with MorphNet
    • MorphNet is a new algorithm which offers techniques to automate the design of efficient neural networks. MorphNet designs a neural network by alternating between a cycle of shrinking and expanding mechanism. The shrinking happens with aid of a sparsifying regularizer and expansion happens via a width multiplier that gets applied to all layers. This has the effect of removing insignificant neurons and allocating more resources to more important neurons, while respecting the resource constraints (such as FLOPs or floating-point operations). When applied to standard networks such as ImageNet, Inception V2, AudioSet or ResNet, the MorphNet discovers novel structures in each case while improving the network performance by a good margin.

Learning Outcome

  • The attendee will see demos of advanced AI applications in Style Art, healthcare, literature, video analytics, robotics, driverless cars etc.

  • Learn to apply advanced AI techniques to solve industry problems such as healthcare radiology, video analysis and synthesis, audio/visual/artistic content generation, data mining in pharma literature, training robots, trajectory planning and obstacle detection in driverless cars, developing efficient neural network models etc.

  • The attendee will learn how to use pre-trained models and algorithms such as FCN/U-Net/DeepLab, Dilated ResNet, DensePose, BERT, ELMO, YOLO, Q-learning, MorphNet etc.

  • Learn advanced Computer Vision techniques around image segmentation, photo-realistic style transfer, pose estimation etc.

  • Review latest NLP solutions for text summarization and video captioning.

  • Understand Reinforcement Learning and how one can use it for factory automation, financial modelling and driverless car control.

  • Learn how to develop faster and efficient neural network architectures using MorphNet algorithm.

  • Learn the cutting-edge AI algorithms in Advanced Vision, NLP and RL and see solution demos around all these algorithms.

Target Audience

AI and ML practitioners, Data Scientists, AI Researchers, IT Managers and Developers

Prerequisites for Attendees

This session is suitable for folks who have some knowledge of Machine Learning and Deep Learning.

schedule Submitted 3 weeks ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  2 weeks ago
    reply Reply

    Dear Vivek: It is impressive to see the breadth of topics you plan to cover in the workshop with demos, each of which are very interesting. To achieve the learning objectives, it appears to me that you might like to cover 1-2 topics in depth, with hands-on sessions. What do you think?

    Warm Regards,

    Vikas

    • Vivek Singhal
      By Vivek Singhal  ~  2 weeks ago
      reply Reply

      Hello Dr Agrawal

      Thanks for your feedback.

      We are hoping to do an AI demo showcase with some state of the art algos. We have a team of 4-5 AI Researchers presenting in this session and hence like to cover 4-5 demos if possible. Since we have a 6 hour session here, perhaps we are able to do so. I was hoping to create a new, unique type of session out of this one.

      What do you think ?

      We could pick and cover the most intuitive demos from each category shown above.

      Eg. From the list provided, we might focus on these ones :-

      VISION

      1) Photo-realistic style transfer

      NLP

      1) QnA system with BERT

      RL

      1) Robotic Arm Simulator

      Driverless Cars

      1) ESPNet Segmentation for driverless car

      Neural Network Architecture

      1) Efficient Neural Networks with MorphNet

      Thoughts ? If you wish to suggest from this list, kindly share your thoughts.

      Thanks,

      Vivek Singhal


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