O’REILLY、INTEL AI主办

English中文
将人工智能用起来
2019年6月18-21日
北京,中国

Schedule: 模型与方法 (Models and Methods) sessions

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09:00 - 17:00 Tuesday, June 18 & Wednesday, June 19
Location: 多功能厅3A+B(Function Room 3A+B)
Jike Chong (Tsinghua University | Acorns), 黄铃 (Tsinghua University), 陈薇 (排列科技)
您想了解金融企业是怎样利用大数据和人工智能技术来画像个人行为并检测欺诈用户的吗?互联网金融幕后的量化分析流程是怎么杨的?个人信用是怎样通过大数据被量化的?在实践过程中,机器学习算法的应用存在着哪些需要关注的方面?怎样通过图谱分析来融合多维数据,为我们区分正常用户和欺诈用户? 这套辅导课基于清华大学交叉信息研究院开设的一门"量化金融信用与风控分析”研究生课。其中会用LendingClub的真实借贷数据做为案例,解说一些具体模型的实现。 了解更多信息.
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09:0012:30 Wednesday, June 19, 2019
Location: 紫金大厅B(Grand Hall B)
Alejandro Saucedo (The Institute for Ethical Ai & Machine Learning)
Undesired bias in machine learning has become a worrying topic due to the numerous high profile incidents. In this talk we demystify machine learning bias through a hands-on example. We'll be tasked to automate the loan approval process for a company, and introduce key tools and techniques from latest research that allow us to assess and mitigate undesired bias in our machine learning models. 了解更多信息.
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09:0012:30 Wednesday, June 19, 2019
Location: 报告厅(Auditorium)
Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft), Henry Zeng (Microsoft)
Almost every business today uses forecasting to make better decisions and allocate resources more effectively. Deep learning has achieved a lot of success in computer vision, text and speech processing, but has only recently been applied to time series forecasting. In this tutorial we show how and when to apply deep neural networks to time series forecasting. The tutorial will be in CHN and EN. 了解更多信息.
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13:3017:00 Wednesday, June 19, 2019
Location: 报告厅(Auditorium)
Sujatha Sagiraju (Microsoft), Henry Zeng (Microsoft)
Intelligent experiences powered by AI can seem like magic to users. Developing them, however, is pretty cumbersome involving a series of sequential and interconnected decisions along the way that are pretty time consuming. What if there was an automated service that identifies the best machine learning pipelines for a given problem/data? Automated machine learning does exactly that! 了解更多信息.
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11:1511:55 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
David Low (Pand.ai)
Transfer Learning has been proven to be a tremendous success in the Computer Vision field as a result of ImageNet competition. In the past months, the Natural Language Processing field has witnessed several breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit and BERT. In this talk, David will be showcasing the use of transfer learning on NLP application with SOTA accuracy. 了解更多信息.
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13:1013:50 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
Bichen Wu (UC Berkeley)
The success of deep neural networks is attributed to three factors: stronger computing capacity, more complex neural networks, and more data. These factors, however, are usually not available with the edge applications as autonomous driving, AR/VR, IoT, and so on. In this talk we discuss how we apply AutoML, SW/HW codesign, domain adaptation to solve these problems. 了解更多信息.
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13:1013:50 Thursday, June 20, 2019
Location: 报告厅(Auditorium)
Prasanth Pulavarthi (Microsoft), Henry Zeng (Microsoft)
An open and interoperable ecosystem enables you to choose the framework that's right for you, train at scale, and deploy to cloud and edge. ONNX provides a common format supported by many popular frameworks and hardware accelerators. This session provides an introduction to ONNX and its core concepts. The session will be delivered in English and Chinese jointly. 了解更多信息.
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13:1013:50 Thursday, June 20, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
Tao Lu (Microsoft), Chenhui Hu (Microsoft)
Forecasting customer activities is one of the most important and common business problems. In Microsoft Azure Identity team, we forecast customer behavior based on billions of user activities. We will share how we improve 25% of forecasting accuracy with dilated convolutional neural networks and reduce 80% of the time in development with the best practices of time series forecasting. 了解更多信息.
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14:0014:40 Thursday, June 20, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
Jialin Jiao (Pony.ai)
While deep learning has been in the center of AI with unprecedentedly great results, predictions of deep neural networks usually do not come with a reliable and well-calibrated confidence score. Wrong but confident predictions place great threads to critical real-life applications, e.g. self-driving car. This talk is a tutorial/comparison of confidence estimation methods for deep neural networks. 了解更多信息.
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14:5015:30 Thursday, June 20, 2019
Location: 报告厅(Auditorium)
Hui Xue (微软亚洲研究院)
人工智能在过去的几年里飞速发展,但是机器学习的实践和应用需要消耗一定的人力和时间。例如,如何去做特征选择,如何设计一个适合该任务的神经网络模型等等。而自动机器学习技术,可以帮助开发者和机器学习实战者,缩短开发周期,提高效率。我们的介绍主要包括:自动机器学习技术的进展;我们开源的自动机器学习开源库Neural Network Intelligence; 如何利用自动机器学习的技术,在产品和应用上提高效率,节省所需的时间和缩短周期。我们会在最后一部分,分享一些利用自动特征选择,自动参数调整以及模型架构搜索上的成功案例。 了解更多信息.
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14:5015:30 Thursday, June 20, 2019
Location: 多功能厅5A+B(Function Room 5A+B)
Guoqiong Song (Intel), Luyang Wang (Office Depot), Jennie Wang (Intel), Jing (Nicole) Kong (Office Depot)
To show case how to build efficient recommender systems for e-commerce industry using deep learning technologies 了解更多信息.
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13:1013:50 Friday, June 21, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
陈玉荣 (Intel)
深度学习在许多领域尤其是视觉识别/理解方面取得了巨大突破,但它在训练和部署方面都存在一些挑战。本讲座将介绍我们通过高效CNN算法设计、领先DNN模型压缩技术和创新部署时DNN网络结构优化来解决深度学习部署挑战的前沿研究成果。 了解更多信息.
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14:0014:40 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Tags: wl
Aileen Nielsen (Skillman Consulting)
Deep learning for time series analysis has made rapid progress in 2018 and 2019, with advances in the use of both convolutional and recurrent neural network architectures. The state of the art in deep forecasting will be summarized for 2018 and 2019, including use cases in both forecasting and generating time series. 了解更多信息.
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14:0014:40 Friday, June 21, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
Mingxi Wu (TigerGraph)
图数据上的非监督学习在激活大数据的经济价值上有着广泛和不可替代的作用。 PageRank能够发掘重要的实体, 社区发掘(community detection)可以找到具有某种特性的群体,紧密度中心性算法(Closeness Centrality)可以自动找到远离群体的个体。所有这些算法都是非监督的学习。 我们分享一些具体客户案例来展示他们的价值,同时分享怎样在大数据上灵活应用这些开源算法。 了解更多信息.
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14:5015:30 Friday, June 21, 2019
Location: 紫金大厅B(Grand Hall B)
Kaz Sato (Google)
Creating an ML model is just a starting point. To bring the technology into production service, you need to solve various real-world issues such as: building a data pipeline for continuous training, automated validation of the model, version control of the model, scalable serving infra, and ongoing operation of the ML infra with monitoring and alerting. 了解更多信息.
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16:2017:00 Friday, June 21, 2019
Location: 多功能厅2(Function Room 2)
Maulik Soneji (Go-jek), Jewel James (Go-jek)
The story of how we 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 了解更多信息.