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。
NIPS 2020, (2020)
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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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.3243-3251, (2020)
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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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)
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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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2952-2960, (2020)
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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Francisco Borges, Georgios Balikas, Marc Brette, Guillaume Kempf, Arvind Srikantan, Matthieu Landos, Darya Brazouskaya, Qianqian Shi
We described an industrial Natural Language Search system integrated as part of Search of a major Customer Relationship Management platform
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Julieta Martinez, Jashan Shewakramani, Ting Wei Liu, Ioan Andrei Bârsan,Wenyuan Zeng,Raquel Urtasun
A recent line of work has focused on vector quantization, which compresses multiple parameters into a single code
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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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Sriram Vasudevan,Krishnaram Kenthapadi
CIKM '20: The 29th ACM International Conference on Information and Knowledge Management Virt..., pp.2773-2780, (2020)
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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Zhe Wang, Liqin Zhao, Biye Jiang, Guorui Zhou, Xiaoqiang Zhu,Kun Gai
We evaluate the Querys Per Seconds and RT of the pre-ranking system that serves with different models
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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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Weihao Gao, Xiangjun Fan,Jiankai Sun, Kai Jia, Wenzhi Xiao, Chong Wang, Xiaobing Liu
We have proposed Deep Retrieval, an end-to-end learnable structure model for largescale recommender systems
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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2291-2299, (2020)
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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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2608-2616, (2020)
We present GrokNet, a deployed image recognition system for commerce applications
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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.3386-3394, (2020)
Using the foundations of constrained optimization, we present a set of blending algorithms, which are optimal under various assumptions
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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
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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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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knowledge discovery and data mining, (2019): 1927-1935
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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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
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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pp.1743-1751 (2019)
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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Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, no. 12 (2019): 3165-3166
Dr Hongxia Yang is working as the Senior Staff Data Scientist and Director in Alibaba Group
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Keywords
E-commerceReal TimeSearch RankingAssisted WritingAssociated ToolAutomationBenchmark PbcBetter DecisionBusiness ImpactClassification Result
Authors
Andrew Tomkins
Paper 2
Kun Gai
Paper 2
Christopher Re
Paper 2
Malay Haldar
Paper 2
Sahin Cem Geyik
Paper 2
Pipei Huang
Paper 2
Dik Lee
Paper 1
Viet Ha-Thuc
Paper 1
Stephen H. Bach
Paper 1