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Data Science Weekly Newsletter
May 2, 2019

Editor's Picks

  • Reinforcement Learning, Fast and Slow
    Our new paper, reviews recent techniques in deep RL that narrow the gap in learning speed between humans and agents, & demonstrate an interplay between fast and slow learning w/ parallels in animal/human cognition...

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • Detailed Human Shape Estimation from Single Image by HMD
    This paper presents a novel framework to recover detailed human body shapes from a single image... we propose a novel learning based framework that combines the robustness of parametric model with the flexibility of free-form 3D deformation. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information...
  • "Self-Supervised Learning"
    I [Yann LeCun] now call it "self-supervised learning", because "unsupervised" is both a loaded and confusing term. In self-supervised learning, the system learns to predict part of its input from other parts of it input. In other words a portion of the input is used as a supervisory signal to a predictor fed with the remaining portion of the input...
  • wav2vec: Unsupervised Pre-training for Speech Recognition
    We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting representations are then used to improve acoustic model training. We pre-train a simple multi-layer convolutional neural network optimized via a noise contrastive binary classification task...
  • Unsupervised Data Augmentation
    Data augmentation is often associated with supervised learning. We find *unsupervised* data augmentation works better. It combines well with transfer learning (e.g. BERT) and improves everything when datasets have a small number of labeled examples...
  • Local Relation Networks for Image Recognition
    This paper presents a new image feature extractor, called the local relation layer, that adaptively determines aggregation weights based on the compositional relationship of local pixel pairs. With this relational approach, it can composite visual elements into higher-level entities in a more efficient manner that benefits semantic inference...


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Predictive Analytics World (PAW) brings together five co-located industry-specific events in Las Vegas: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare and Deep Learning World, gathering the top practitioners and the leading experts in data science and machine learning. By design, this mega-conference is where to meet the who's who and keep up on the latest techniques, making it the leading machine learning event. On stage: Google, Apple, Uber, Facebook, LinkedIn, Twitter and more...
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  • Data Scientist - TRANZACT - Fort Lee, NJ or Raleigh, NC

    Tranzact is a fast paced, entrepreneurial company offering a well-rounded suite of marketing solutions to help insurance companies stay ahead of the competition. The Data Scientist will be solving the toughest problems at Tranzact by using data. More specifically, responsible for gathering data, conducting analysis, building predictive algorithms and communicating findings to drive profitable growth and performance across Tranzact. Must have a strong grasp on the data structure, business needs, and statistical and predictive modeling...

Training & Resources

  • Choice of Symplectic Integrator in Hamiltonian Monte Carlo
    This is a bit of a deep dive into our choice of integrator in Hamiltonian Monte Carlo (HMC). As a spoiler alert, we find that the leapfrog integrator is empirically the fastest, or at least no slower, than other integrators. It is still interesting to consider what choice we have made, and why we have made it...


  • Reproducible Research with R and R Studio
    "a very practical book that teaches good practice in organizing reproducible data analysis and comes with a series of examples..."...
    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page

    P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian

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