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Data Science Weekly Newsletter
May 2, 2019
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...
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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...
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...
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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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 .
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