Data Processing Paper Reading Club

Sino Data Scientist Association(SDSA)

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Deep learning

• Deep Learning for Classical Japanese Literature, Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha [pdf]

Database

• Bigtable: A Distributed Storage System for Structured Data, Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber [pdf]

Data Engineering

• ✔️ Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma,Murphy McCauley, Michael J. Franklin, Scott Shenker, Ion Stoica [pdf]

• MillWheel: Fault-Tolerant Stream Processing at Internet Scale, Tyler Akidau, Alex Balikov, Kaya Bekiroglu, Slava Chernyak, Josh Haberman, Reuven Lax, Sam McVeety, Daniel Mills, Paul Nordstrom, Sam Whittle [pdf]

• Dremel: Interactive Analysis of Web-Scale Datasets, Sergey Melnik, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, Theo Vassilakis, Google, Inc. [pdf]

• The Google File System, Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung [pdf]

• MapReduce: Simplified Data Processing on Large Clusters, Jeffrey Dean and Sanjay Ghemawat [pdf]

Data Bank

• ✔️ Bigtable: A Distributed Storage System for Structured Data, Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber [pdf]

Data Processing

• ✔️ Condition monitoring of bearings under medium and low rotational speed, Carina Freitas, Paulo Morais, Jacques Cuenca, Agusmian Partogi Ompusunggu, Mathieu Sarrazin, Karl Janssens [pdf]

Neuro-linguistic Programming

• Phrase-Based & Neural Unsupervised Machine Translation (2018), Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato [pdf]

Neural Processes

• Neural Processes, Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh [pdf]
• Conditional Neural Processes, Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami [pdf]

Recommendation System

• ✔️ Real-time Personalization using Embeddings for Search Ranking at Airbnb (2018), Mihajlo Grbovic, Haibin Cheng [pdf]

• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (2018) Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, Guangzhong Sun [pdf]

• Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi et al. [pdf]
  PyTorch and Caffee implementation (https://github.com/facebookresearch/dlrm)

Natural Language Processing

• Distributed Representations of Words and Phrases and their Compositionality (2013) Tomas Mikolov et al. [pdf]

• ✔️ Attention Is All You Need (2017), Ashish Vaswani, et al. [pdf] github link (https://github.com/Kyubyong/transformer)
  Google's implementation (https://github.com/tensorflow/tensor2tensor) [Discussion date: 2019.02.03]

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018), Jacob Devlin et al. [pdf]

Computer Vision

• Siamese Neural Networks for One-shot Image Recognition (2015), Gregory Koch, Richard Zemel, Ruslan Salakhutdinov [pdf]
  PyTorch impelmentation (https://github.com/adambielski/siamese-triplet)
  TensorFlow implementation (https://github.com/torrvision/siamfc-tf)

• You Only Look Once: Unified, Real-Time Object Detection (2016), Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi [pdf]
  TensorFlow implementaion (https://github.com/nilboy/tensorflow-yolo)
  ________
  YOLO v2 YOLO9000: Better, Faster, Stronger [pdf]
  PyTorch implementation (https://github.com/ruiminshen/yolo2-pytorch)
  ________
  YOLO v3 YOLOv3: An Incremental Improvement [pdf]
  PyTorch implementation (https://github.com/ayooshkathuria/pytorch-yolo-v3)