14–17 Oct 2019

Schedule: Computer Vision sessions

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9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Hilton Meeting Room 3/4
Umberto Michelucci (TOELT LLC)
Average rating: ****.
(4.00, 1 rating)
Convolutional neural networks (CNNs) are the basis of many algorithms that deal with images, from image recognition and classification to object detection. Using practical examples, Umberto Michelucci walks you through developing convolutional neural networks, using pretrained networks, and even teaching a network to paint. TensorFlow or Keras will be used for all examples. Read more.
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9:0012:30 Tuesday, 15 October 2019
Location: Windsor Suite
Danielle Dean (iRobot), Mathew Salvaris (Microsoft), Wee Hyong Tok (Microsoft)
Average rating: ****.
(4.33, 6 ratings)
Danielle Dean, Mathew Salvaris, and Wee Hyong Tok outline the recommended ways to train and deploy Python models on Azure, ranging from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes. Read more.
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11:0511:45 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Qun Ying (Microsoft)
Average rating: *****
(5.00, 2 ratings)
Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and explain how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention. Read more.
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11:5512:35 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Biraja Ghoshal (Tata Consultancy Service)
Average rating: *....
(1.00, 2 ratings)
Deep learning, which involves powerful black box predictors, has achieved state-of-the-art performance in medical imaging analysis, such as segmentation and classification for diagnosis, but knowing how much confidence there is in a prediction is essential for gaining clinicians' trust. Biraja Ghoshal explores probabilistic modeling with TensorFlow Probability in cancer prediction. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Paris Buttfield-Addison (Secret Lab), Tim Nugent (Lonely Coffee)
Average rating: ****.
(4.75, 8 ratings)
On-device ML and AI is the future for privacy-conscious, cloud-averse users of modern smartphones. Paris Buttfield-Addison and Tim Nugent explore what's possible using CoreML, Swift, and associated frameworks in tandem with the powerful ML-tuned silicon in modern Apple iOS hardware. They demonstrate and create ML and AI features with Swift to show how much you can do without touching the cloud. Read more.
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16:5017:30 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Average rating: ****.
(4.25, 12 ratings)
Developing perception algorithms for autonomous vehicles is incredibly difficult, as they need to operate in thousands of driving conditions and locations. Adam Grzywaczewski explores the challenges involved in data collection, processing, and management, as well as model development and validation. He also provides an overview of the necessary hardware and software infrastructure. Read more.
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10:1010:25 Thursday, 17 October 2019
Location: King's Suite
Raffaello D’Andrea (Verity | ETH Zurich)
Average rating: ****.
(4.69, 13 ratings)
It's hard ignore the attention given to autonomy and robotics. The impact is significant and the reach is extensive, hitting transportation with self-driving cars, logistics and supply with mobile robots, and remote sensing applications with aerial vehicles or drones. Raffaello D'Andrea explores how autonomous indoor drones will drive the next wave of autonomous robotics development and growth. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Westminster Suite
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Average rating: ****.
(4.80, 5 ratings)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Ganes Kesari (Gramener), Soumya Ranjan (Gramener)
Average rating: *****
(5.00, 1 rating)
In many countries, policy decisions are disconnected from data, and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Laurence Moroney (Google)
Average rating: ****.
(4.86, 7 ratings)
Laurence Moroney explores how to go from wondering what machine learning (ML) is to building a convolutional neural network to recognize and categorize images. With this, you'll gain the foundation to understand how to use ML and AI in apps all the way from the enterprise cloud down to tiny microcontrollers using the same code. Read more.

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