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Data science & machine learning in production机器学习模型可以增企业的几乎所有方面，从市场营销到销售再到维护。在生产制造业，物联网的兴起及其带来的前所未有的海量数据，为利用机器学习带来了无数机会。根据《全球市场观察》的一份报告，全球制造业机器学习将从2018年的10亿美元飙升至2025年的160亿美元。除此之外，还需要不断降低成本，促进工业4.0技术的应用。具体来说，在预测性维护、质量控制、物流及存货管理等领域，深度学习都有了广泛的应用。
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Language Models are Few-Shot Learners
Brown Tom B.,Mann Benjamin,Ryder Nick,Subbiah Melanie,Kaplan Jared,Dhariwal Prafulla,Neelakantan Arvind,Shyam Pranav,Sastry Girish,Askell Amanda,Agarwal Sandhini,Herbert-Voss Ariel
NeurIPS, (2020)
Methods
Conclusion
We show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches
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Cited by180BibtexViews727DOI
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BusTr: Predicting Bus Travel Times from Real-Time Traffic
Richard Barnes,Senaka Buthpitiya,James Cook,Alex Fabrikant,Andrew Tomkins, Fangzhou Xu
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.3243-3251, (2020)
Methods
Conclusion
We have described a new model, BusTr, for predicting how long it will take public transit buses to travel between points on their routes based on contextual features such as location and time as well as estimates of current tra c conditions
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Cited by0BibtexViews41DOI
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Shop The Look: Building a Large Scale Visual Shopping System at Pinterest
Raymond Shiau,Hao-Yu Wu,Eric Kim, Yue Li Du, Anqi Guo,Zhiyuan Zhang,Eileen Li,Kunlong Gu, Charles Rosenberg,Andrew Zhai
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.3203-3212, (2020)
Conclusion
We show in the offline evaluations the complete evolution of our embeddings, each annotated with version number and its retrieval metric, and the results of selected versions in end-to-end human relevance evaluations and engagement A/B experiments
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Cited by0BibtexViews47DOI
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Managing Diversity in Airbnb Search
Mustafa Abdool,Malay Haldar,Prashant Ramanathan,Tyler Sax,Lanbo Zhang, Aamir Manasawala, Shulin Yang,Bradley Turnbull,Qing Zhang,Thomas Legrand
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2952-2960, (2020)
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Conclusion
We describe our journey in tackling the problem of diversity for Airbnb search, starting from heuristic based approaches and concluding with a novel deep learning solution that produces an embedding of the entire query context by leveraging Recurrent Neural Networks
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Cited by0BibtexViews86DOI
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Query Understanding for Natural Language Enterprise Search
Francisco Borges, Georgios Balikas, Marc Brette, Guillaume Kempf, Arvind Srikantan, Matthieu Landos, Darya Brazouskaya, Qianqian Shi
Conclusion
We described an industrial Natural Language Search system integrated as part of Search of a major Customer Relationship Management platform
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Cited by0BibtexViews11DOI
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Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Julieta Martinez, Jashan Shewakramani, Ting Wei Liu, Ioan Andrei Bârsan,Wenyuan Zeng,Raquel Urtasun
Conclusion
A recent line of work has focused on vector quantization, which compresses multiple parameters into a single code
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Cited by0BibtexViews21DOI
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Deploying Lifelong Open-Domain Dialogue Learning
Kurt Shuster, Jack Urbanek,Emily Dinan,Arthur Szlam,Jason Weston
Methods
Conclusion
Detailed experiments showed that the one can collect high quality data that improves both automatic offline metrics and user engagement metrics when used for training models
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Cited by0BibtexViews47DOI
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LiFT: A Scalable Framework for Measuring Fairness in ML Applications
Sriram Vasudevan,Krishnaram Kenthapadi
CIKM '20: The 29th ACM International Conference on Information and Knowledge Management Virt..., pp.2773-2780, (2020)
Methods
Conclusion
Considering the importance of measuring and mitigating algorithmic bias in large-scale ML based applications, we presented the LinkedIn Fairness Toolkit, a system for scalable and flexible computation of fairness metrics during different stages of the ML lifecycle
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Cited by0BibtexViews13DOI
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COLD: Towards the Next Generation of Pre-Ranking System
Zhe Wang, Liqin Zhao, Biye Jiang, Guorui Zhou, Xiaoqiang Zhu,Kun Gai
Methods
Conclusion
We evaluate the Querys Per Seconds and RT of the pre-ranking system that serves with different models
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Cited by0BibtexViews38DOI
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Photon: A Robust Cross-Domain Text-to-SQL System
Jichuan Zeng,Xi Victoria Lin,Caiming Xiong,Richard Socher,Michael R. Lyu,Irwin King,Steven C. H. Hoi
Methods
Conclusion
We plan to improve the performance of core models in PHOTON, such as semantic parsing, response generation and context-aware user interaction
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Cited by0BibtexViews76DOI
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Deep Retrieval: An End-to-End Learnable Structure Model for Large-Scale Recommendations
Weihao Gao, Xiangjun Fan,Jiankai Sun, Kai Jia, Wenzhi Xiao, Chong Wang, Xiaobing Liu
Methods
Conclusion
We have proposed Deep Retrieval, an end-to-end learnable structure model for largescale recommender systems
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Cited by0BibtexViews14DOI
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Temporal-Contextual Recommendation in Real-Time
Yifei Ma,Murali Balakrishnan Narayanaswamy,Haibin Lin,Hao Ding
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2291-2299, (2020)
Methods
Conclusion
HRNNs achieve SOTA results on numerous datasets and have been deployed in production at scale for customers to use at Amazon Web Services
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Cited by0BibtexViews60DOI
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GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce
Sean Bell,Yiqun Liu,Sami Alsheikh,Yina Tang,Ed Pizzi, Michael Henning,Karun Singh,Omkar Parkhi,Fedor Borisyuk
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2608-2616, (2020)
Methods
Conclusion
We present GrokNet, a deployed image recognition system for commerce applications
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Cited by0BibtexViews30DOI
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Ads Allocation in Feed via Constrained Optimization
Jinyun Yan,Zhiyuan Xu,Birjodh Tiwana,Shaunak Chatterjee
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.3386-3394, (2020)
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Conclusion
Using the foundations of constrained optimization, we present a set of blending algorithms, which are optimal under various assumptions
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Cited by0BibtexViews28DOI
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Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Stephen H. Bach, Daniel Rodriguez, Yintao Liu,Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen,Alexander Ratner,Braden Hancock, Houman Alborzi, Rahul Kuchhal,Christopher Ré
Proceedings of the 2019 International Conference on Management of Data, (2019): 362-375
Methods
Conclusion
In this paper we presented the first results from deploying the Snorkel DryBell framework for weakly supervised machine learning in a large-scale, industrial setting
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Cited by25BibtexViews57DOI
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Gmail Smart Compose: Real-Time Assisted Writing
Mia Xu Chen,Benjamin N. Lee,Gagan Bansal,Yuan Cao,Shuyuan Zhang,Justin Lu,Jackie Tsay,Yinan Wang,Andrew M. Dai,Zhifeng Chen,Timothy Sohn,Yonghui Wu
KDD, (2019)
Methods
Conclusion
In this paper we described Smart Compose, a novel system that improves Gmail users’ writing experience by providing real-time, context-dependent and diverse suggestions as users type
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Cited by16BibtexViews127DOI
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Applying Deep Learning To Airbnb Search
Malay Haldar,Mustafa Abdool,Prashant Ramanathan,Tao Xu,Shulin Yang,Huizhong Duan,Qing Zhang,Nick Barrow-Williams,Bradley C. Turnbull,Brendan M. Collins,Thomas Legrand
knowledge discovery and data mining, (2019): 1927-1935
Conclusion
Similarity of the listing to the past views of the user, computed based on co-view embeddings. These models tap into data that isn’t directly part of the search ranking training examples, providing the DNN with additional information
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Cited by14BibtexViews17DOI
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Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Qiwei Chen, Huan Zhao,Wei Li,Pipei Huang,Wenwu Ou
Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse ..., (2019): 12
Methods
Conclusion
We propose to use the powerful Transformer model to capture the sequential signals underlying users' behavior sequences for recommendation in Alibaba
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Cited by12BibtexViews28DOI
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150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com
Lucas Bernardi,Themistoklis Mavridis,Pablo Estevez
pp.1743-1751 (2019)
Conclusion
In a standard Randomized Controlled Trials, the population is divided into control and treatment groups, all subjects in the treatment group are exposed to the change, and all subjects in the control group are exposed to no change
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Cited by8BibtexViews10DOI
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AliGraph: A Comprehensive Graph Neural Network Platform
Rong Zhu,Kun Zhao,Hongxia Yang,Wei Lin,Chang Zhou,Baole Ai, Yong Li,Jingren Zhou
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, no. 12 (2019): 3165-3166
Conclusion
Dr Hongxia Yang is working as the Senior Staff Data Scientist and Director in Alibaba Group
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Cited by8BibtexViews88DOI
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