14–17 Oct 2019

Schedule: Machine Learning sessions

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9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Park Suite
Michael Cullan (Pragmatic Institute)
Average rating: ****.
(4.00, 1 rating)
The TensorFlow library provides computational graphs with automatic parallelization across resources—ideal architecture for implementing neural networks. Michael Cullan walks you through TensorFlow's capabilities in Python, from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.
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9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Westminster Suite
Rich Ott (The Pragmatic Institute)
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Join Rich Ott to get the knowledge you need to build deep learning models using real-world datasets and PyTorch. Read more.
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9:00 - 17:00 Monday, 14 October & Tuesday, 15 October
Location: Hilton Meeting Room 1/2
Angie Ma (Faculty), Richard Sargeant (Faculty), Joshua Muncke (Faculty Science Ltd)
Average rating: *****
(5.00, 2 ratings)
Angie Ma and Richard Sargeant offer a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization. Read more.
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9:0012:30 Tuesday, 15 October 2019
Location: Buckingham Room - Palace Suite
Ira Cohen (Anodot), Arun Kejariwal (Independent)
Average rating: ***..
(3.25, 8 ratings)
While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores the cycle of developing ML-based capabilities (or entire products) and the role of the (product) manager in each step of the cycle. 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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9:0012:30 Tuesday, 15 October 2019
Location: Blenheim Room - Palace Suite
Edward Oakes (UC Berkeley Electrical Engineering & Computer Sciences), Peter Schafhalter (UC Berkeley RISELab), Kristian Hartikainen (University of Oxford)
Average rating: *****
(5.00, 5 ratings)
Edward Oakes, Peter Schafhalter, and Kristian Hartikainen take a deep dive into Ray, a new distributed execution framework for distributed AI applications developed by machine learning and systems researchers at RISELab, and explore Ray’s API and system architecture and sharing application examples, including several state-of-the-art distributed training, hyperparameter search, and RL algorithms. Read more.
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13:3017:00 Tuesday, 15 October 2019
Location: Buckingham Room - Palace Suite
Pramod Singh (Walmart Labs ), Akshay Kulkarni (Publicis Sapient)
Average rating: **...
(2.40, 10 ratings)
An estimated 80% of data generated is an unstructured format, such as text, an image, audio, or video. Vijay Srinivas Agneeswaran, Pramod Singh, and Akshay Kulkarni explore how to create a language model that generates natural language text by implementing and forming a recurrent neural network and attention networks built on top of TensorFlow 2.0. Read more.
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13:3017:00 Tuesday, 15 October 2019
Location: Windsor Suite
Sergey Ermolin (Amazon Web Services), Vineet Khare (Amazon Web Services)
Average rating: *....
(1.50, 4 ratings)
Sergey Ermolin and Vineet Khare provide a step-by-step overview on how to implement, train, and deploy a reinforcement learning (RL)-based recommender system with real-time multivariate optimization. They show you how leverage RL to implement a recommender system that optimizes an advertisement message that promotes adoption of merchant's services. Read more.
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10:1510:30 Wednesday, 16 October 2019
Location: King's Suite
Jeff Jonas (Senzing)
Average rating: ****.
(4.36, 11 ratings)
Entity resolution—determining “who is who” and “who is related to whom”—is essential to almost every industry, including banking, insurance, healthcare, marketing, telecommunications, social services, and more. Jeff Jonas details how you can use a purpose-built real-time AI, created for general-purpose entity resolution, to gain new insights and make better decisions faster. Read more.
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11:0511:45 Wednesday, 16 October 2019
Location: Westminster Suite
Adithya Hrushikesh (Vodafone)
Average rating: ****.
(4.00, 4 ratings)
Every day, millions of Vodafone Germany customers reach out through various social media channels about issues related to mobile, internet, signal issues, etc. Adithya Hrushikesh details how to build and deploy an ensemble model to classify 26 (originally 56) complaint classes using machine learning over deep learning. He also touches on the business case, data product development, and GDPR. 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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11:0511:45 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Alex Ingerman (Google)
Average rating: ****.
(4.29, 7 ratings)
Federated learning is the approach of training ML models across many devices without collecting the data in a central location. Alex Ingerman explores learning concepts and the use cases for decentralized machine learning, drawing on Google's real-world deployments. You'll learn how to build your first federated models with the open source TensorFlow Federated. Read more.
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11:5512:35 Wednesday, 16 October 2019
Location: Westminster Suite
Martin Benson (Jaywing)
Machine learning has been used in credit scoring for three decades. Martin Benson discusses the history of machine learning in credit scoring and the need for explainable and justified decisions made by machine learning systems. Come find out if it's possible to overcome the black box problem and learn how machine learning systems are evolving and how to bypass the challenges to adoption. 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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11:5512:35 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Average rating: ****.
(4.00, 5 ratings)
Sequence to sequence (S2S) modeling using neural networks has become increasingly mainstream in recent years. In particular, it's been used for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for these use cases, visualization, real-time anomaly detection, and forecasting. Read more.
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11:5512:35 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Chang Liu (Georgian Partners ), Ji Chao Zhang (Georgian Partners)
Average rating: ****.
(4.33, 3 ratings)
The world is increasingly data driven, and people have developed an awareness and concern for their data. Chang Liu and Ji Chao Zhang examine differential privacy—the component of the TensorFlow Privacy library that allows users to train differentially private logistic regression and support vector machines—along with real-world use cases and demonstrations for how to apply the tools. 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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13:4514:25 Wednesday, 16 October 2019
Location: Westminster Suite
Martin Goodson (Evolution AI)
Average rating: ****.
(4.75, 4 ratings)
Data leakage occurs when the model gains access to data that it shouldn't have. AI systems can fail catastrophically in production if leakage is not dealt with properly. Martin Goodson details the four main manifestations of data leakage and explains how to recognize the warning signs. By mastering several key scientific principles, you can mitigate the risk of failure. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Average rating: ****.
(4.67, 3 ratings)
Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Douglas Calegari (Independent)
Average rating: ****.
(4.00, 3 ratings)
Douglas Calegari details a solution that classifies and routes emails coming into a busy insurance service center. Join in to discover how his team evaluated NLP models, leveraged various techniques to increase classification and entity recognition accuracy, designed a scalable end-to-end machine learning data pipeline, and integrated them into an existing transactional system. Read more.
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14:3515:15 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Abhishek Kumar (Publicis Sapient)
Abhishek Kumar outlines how to industrialize capsule networks by detailing capsule networks and how capsule networks help handle spatial relationships between objects in an image and how to apply them to text analytics and tasks such as NLU or summarization. Join in to see a scalable, productionizable implementation of capsule networks over KubeFlow. Read more.
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14:3515:15 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Danielle Dean (iRobot), Wee Hyong Tok (Microsoft), Mathew Salvaris (Microsoft)
Average rating: ****.
(4.00, 2 ratings)
Dive into the the newly released GitHub repository for recommended ways to train and deploy models on Azure with Danielle Dean, Wee Hyong Tok, and Mathew Salvaris. The repository ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes. Read more.
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14:3515:15 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Arun Verma (Bloomberg)
Average rating: ***..
(3.50, 4 ratings)
To gain an edge in the markets, quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly nontraditional sources of data. Arun Verma shares NLP, AI, and ML techniques that help extract derived signals that have significant trading alpha or risk premium and lead to profitable trading strategies. Read more.
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16:0016:40 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
Average rating: ****.
(4.00, 2 ratings)
AI-powered market research is performed by indirect approaches based on sparse and implicit consumer feedback (e.g., social network interactions, web browsing, or online purchases). These approaches are more scalable, authentic, and suitable for real-time consumer insights. Gianmario Spacagna proposes a novel algorithm of audience projection able to provide consumer insights over multiple domains. Read more.
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16:0016:40 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Rajib Biswas (Ericsson)
Average rating: ****.
(4.00, 2 ratings)
Rajib Biswas outlines the application of AI algorithms like generative adversarial networks (GANs) to solve natural language synthesis tasks. Join in to learn how AI can accomplish complex tasks like machine translation, write poetry with style, read a novel, and answer your questions. Read more.
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16:0016:40 Wednesday, 16 October 2019
Location: Westminster Suite
Danielle Deibler (MarvelousAI)
Average rating: ****.
(4.57, 7 ratings)
Danielle Deibler examines an approach to detecting bias, fine-grained emotional sentiment, and misinformation through the detection of political narratives in online media. As building blocks, the methodology uses human-in-the-loop, alongside other natural language processing and computational linguistics techniques, with examples focused on the 2020 US presidential election. 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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16:5017:30 Wednesday, 16 October 2019
Location: Buckingham Room - Palace Suite
James Fletcher (Grakn)
Average rating: ***..
(3.50, 2 ratings)
Statistical approaches alone are not sufficient to tackle the complexity of AI challenges today. Being smarter with the data we already have is critical to achieving machine understanding of any complex domain. James Fletcher explains how knowledge graph convolutional networks (KGCNs) demonstrate the usefulness of combining a connectionist deep learning approach with a symbolic approach. Read more.
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16:5017:30 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Bruno Wassermann (IBM Research)
Average rating: ****.
(4.00, 2 ratings)
Imagine there's a new version of your complex machine learning pipeline, but you need to make sure it doesn't negatively impact the performance of large numbers of existing customer models in production. Bruno Wassermann explains how IBM Research tackled the challenge for the natural language understanding layer of the IBM Watson Assistant service and demonstrates a new tool called Clue. Read more.
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9:059:20 Thursday, 17 October 2019
Location: King's Suite
Zhe Zhang (LinkedIn)
Average rating: ****.
(4.12, 8 ratings)
From people you may know (PYMK) to economic graph research, machine learning is the oxygen that powers how LinkedIn serves its 630M+ members. Zhe Zhang provides you with an architectural overview of LinkedIn’s typical machine learning pipelines complemented with key types of ML use cases. Read more.
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9:309:45 Thursday, 17 October 2019
Location: King's Suite
Ihab Ilyas (University of Waterloo)
Average rating: ****.
(4.30, 10 ratings)
Ihab Ilyas highlights the data-quality problem and describes the HoloClean framework, a state-of-the-art prediction engine for structured data with direct applications in detecting and repairing data errors, as well as imputing missing labels and values. Read more.
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11:0511:45 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Zaid Tashman (Accenture Labs)
Average rating: *****
(5.00, 3 ratings)
Today traditional approaches to predictive maintenance fall short. Zaid Tashman dives into a novel approach to predict rare events using a probabilistic model, the mixed membership hidden Markov model, highlighting the model's interpretability, its ability to incorporate expert knowledge, and how the model was used to predict the failure of water pumps in developing countries. Read more.
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11:0511:45 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Ted Dunning (MapR, now part of HPE)
Average rating: ****.
(4.50, 4 ratings)
Evaluating machine learning models is surprisingly hard, but it gets even harder because these systems interact in very subtle ways. Ted Dunning breaks the problem into operational and functional concerns and shows you how each can be done without unnecessary pain and suffering. You'll also get to see some exciting visualization techniques to help make the differences strikingly apparent. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Jewel James (Gojek), Mudit Maheshwari (Gojek)
Average rating: ****.
(4.00, 2 ratings)
GoFood, Gojek's food delivery product, is one of the largest of its kind in the world. Jewel James and Mudit Maheshwari explain how they prototyped the search framework that personalizes the restaurant search results by using ML to learn what constitutes a relevant restaurant given a user's purchasing history. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Windsor Suite
Katharine Jarmul (KIProtect)
Average rating: *****
(5.00, 2 ratings)
Katharine Jarmul sates your curiosity about how far we've come in implementing privacy within machine learning systems. She dives into recent advances in privacy measurements and explains how this changed the approach of privacy in machine learning. You'll discover new techniques including differentially private data collection, federated learning, and homomorphic techniques. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Alasdair Allan (Babilim Light Industries)
Average rating: ****.
(4.50, 4 ratings)
The future of machine learning is on the edge and on small, embedded devices that can run for a year or more on a single coin-cell battery. Alasdair Allan dives deep into how using deep learning can be very energy efficient and allows you to make sense of sensor data in real time. 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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11:5512:35 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Julien Simon (AWS)
Average rating: ****.
(4.86, 7 ratings)
Many natural language processing (NLP) tasks require each word in the input text to be mapped to a vector of real numbers. Julien Simon explores word vectors, why they’re so important, and which are the most popular algorithms to compute them (Word2Vec, GloVe, BERT). You'll get to see how to solve typical NLP problems through several demos by either computing embeddings or reusing pretrained ones. Read more.
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13:4514:25 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Sridhar Alla (BlueWhale)
Average rating: *....
(1.67, 3 ratings)
Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Average rating: ****.
(4.00, 4 ratings)
Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Carlos Rodrigues (Siemens)
Average rating: *****
(5.00, 3 ratings)
An evolving landscape of cyber threats demands innovation. It's time to bring AI to the fight. Carlos Rodrigues explains why it's mandatory to use bleeding-edge AI in production to improve threat detection in a worldwide company such as Siemens. The corporate network has more than 500,000 endpoint and more than 370,000 employees. The attack vectors are endless; thus, legacy approaches don't scale. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Ilya Feige (Faculty)
Average rating: *****
(5.00, 2 ratings)
Ilya Feige explores AI safety concerns—explainability, fairness, and robustness—relevant for machine learning (ML) models in use today. With concepts and examples, he demonstrates tools developed at Faculty to ensure black box algorithms make interpretable decisions, do not discriminate unfairly, and are robust to perturbed data. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: Windsor Suite
Paco Nathan (derwen.ai)
Average rating: ****.
(4.50, 2 ratings)
Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Paris Buttfield-Addison (Secret Lab), Tim Nugent (Lonely Coffee)
Average rating: ****.
(4.80, 5 ratings)
You're building a high-volume, expensive, robot-driven warehouse. Your robots need to get to the right place quickly, find the right item, and sort it to the right place without colliding with each other, the shelves, or people. But you don't have any robots, and you need to start writing the logic and training them. Paris Buttfield-Addison and Tim Nugent outline how to use a simulation to do it. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Manas Ranjan Kar (Episource)
Natural language processing (NLP) is hard, especially for clinical text. Manas Ranjan Kar explains the multiple challenges of NLP for clinical text and why it's so important that we invest a fair amount of time on domain-specific feature engineering. It’s also crucial to understand to diagnose an NLP model performance and identify possible gaps. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Tuhin Sharma (Binaize), Bargava Subramanian (Binaize)
Average rating: ****.
(4.50, 2 ratings)
There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learning—which is privacy preserving and doesn't require data to be moved to the cloud—for data quality and cybersecurity. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Weifeng Zhong (Mercatus Center at George Mason University)
Average rating: ****.
(4.00, 1 rating)
Weifeng Zhong explores a novel method to learn structural changes embedded in unstructured texts based on the Policy Change Index (PCI) framework developed by economists Julian Chan and Weifeng Zhong. He explains how an off-the-shelf application of deep learning—with an important twist—can help you detect structural break points in time series text data. Read more.
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16:5017:30 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Tom Sabo (SAS)
Average rating: ****.
(4.50, 2 ratings)
Efforts to counter human trafficking internationally must assess data from a variety of sources to determine where best to devote limited resources. Tom Sabo explores text-based machine learning, rule-based text extraction to generate training data for modeling efforts, and interactive visualization to improve international trafficking response. Read more.
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16:5017:30 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Vignesh Gopakumar (United Kingdom Atomic Energy Authority)
Vignesh Gopakumar explores image mapping of the temporal evolution of physics parameters as plasma interacts with the reactor wall using a data-inferred approach. The model captures how parameters such as temperature and density evolve across space and time. By analyzing the patterns found in simulation data, the model learns the existing physics relations implicitly defined within the data. Read more.

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