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2(Function Room 2)
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)
Numerous high-profile incidents have proved undesired bias in machine learning a worrying topic. Alejandro Saucedo uses a hands-on example to demystify machine learning bias. You'll automate the loan-approval process for a company and explore key tools and techniques from the latest research that allows you to assess and mitigate undesired bias in machine learning models. 了解更多信息.
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13:3017:00 Wednesday, June 19, 2019
Location: 报告厅(Auditorium)
Henry Zeng (Microsoft), Lu Zhang (Microsoft), xiao zhang (Microsoft)
Average rating: ****.
(4.00, 3 ratings)
Intelligent experiences powered by AI seem like magic, but developing them is cumbersome, involving a series of time consuming sequential and interconnected decisions along the way. What if you had an automated service that could identify the best machine learning pipelines for your given problem or data? Lu Zhang, Henry Zeng, and Xiao Zhang detail how automated machine learning does that. 了解更多信息.
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11:1511:55 Thursday, June 20, 2019
Location: 紫金大厅B(Grand Hall B)
Tao Lu (Microsoft), Chenhui Hu (Microsoft)
Average rating: ***..
(3.75, 4 ratings)
Forecasting customer activities is an important, common business problem, and Tao Lu and Chenhui Hu forecast customer behavior based on billions of user activities. Join them as they share how Microsoft improved forecasting accuracy by 25% with dilated convolutional neural networks and reduced time in development by 80% with a set of time series forecasting best practices. 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)
Average rating: *****
(5.00, 1 rating)
Transfer learning has been a tremendous success in computer vision as a result of the ImageNet competition. In the past few months, natural language processing (NLP) has witnessed several breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit, and BERT. Join David Low as he showcases the use of transfer learning on NLP applications with state-of-the-art accuracy. 了解更多信息.
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13:1013:50 Thursday, June 20, 2019
Location: 报告厅(Auditorium)
Henry Zeng (Microsoft), Klein Hu (Microsoft), Emma Ning (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. Henry Zeng, Klein Hu, and Emma Ning introduce you to ONNX and its core concepts. (Presented in English and Chinese.) 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)
姜涛 (Kwai)
介绍如何综合应用多项人工智能技术进行K歌修音和短视频自动配乐,涉及的相关技术包括:人声/音乐分离、高精度的基频提取、自动作曲/作词技术、基于视频内容的音乐生成等。 了解更多信息.
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14:0014:40 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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14:0014:40 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
Arun Verma (Bloomberg)
Average rating: ****.
(4.00, 1 rating)
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 details AI and machine learning (ML) techniques in quantitative finance that lead to profitable trading strategies. 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)
peng ni (凡普金科集团有限公司)
该议题主要包括:1. 语音切分技术的原理和应用;2. 语音识别模型的构建优化;3. 语音情感分析构建应用;4. 语音数据的实时处理框架;5. 金融场景业务落地。 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)
Kai Huang (Intel)
Real-time recommender systems are critical for the success of the ecommerce industry. Join Kai Huang, Luyang Wang, and Jing Kong as they showcase how to build efficient recommender systems for the ecommerce industry using deep learning technologies. 了解更多信息.
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14:5015:30 Thursday, June 20, 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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16:2017:00 Thursday, June 20, 2019
Location: 多功能厅2(Function Room 2)
Maulik Soneji (GO-JEK), Jewel James (Gojek)
Hear how Maulik Soneji and Jewel James prototyped the search framework that personalizes the restaurant search results by using machine learning (ML) to learn what constitutes a relevant restaurant given a user's purchasing history. 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)
Kaz Sato (Google)
Average rating: *****
(5.00, 1 rating)
Kaz Sato explains how 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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14:0014:40 Friday, June 21, 2019
Location: 多功能厅6A+B (Function Room 6A+B)
Dingxian Wang (eBay)
In recent years, there's been increasing attention on incorporating knowledge graphs into recommender systems. By exploring the interlinks within a knowledge graph, you can discover the connectivity between users and items as paths. Dingxian Wang outlines a new model, knowledge-aware path recurrent network (KPRN), for exploiting knowledge graphs for recommendation. 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)
Le Zhang (Microsoft), Jianxun Lian (Microsoft)
Enterprises benefit from recommendation systems for revenue and customer engagement, but creating such a system is time-consuming. Le Zhang and Jianxun Lian explore the Microsoft/Recommenders repository, which offers solutions to building recommendation systems. It contains classic and state-of-the-art algorithms from Microsoft and enables enterprise success by leveraging Azure's cloud capability. 了解更多信息.