Top 10 curated AI reads for November 2018
November, 2018
News about Machine Learning (ML), Artificial Intelligence (AI), Data Science (DS) and related advanced analytics areas.
Welcome to xplore.ai’s fifth post of the 10 curated AI reads monthly series. The objective of these series is to provide the audience with a curated list of the most interesting news, publishings and tools that our team have ran into during the previous month.
Computer Vision
1. European Conference on Computer Vision (ECCV) 2018 Summary
2018 edition of European Conference on Computer Vision (ECCV) insights by Scortex. In summary, very interesting findings regarding Group Normalization (GN) which improves object detection algorithms, Hourglass Networks in which Corner Net leverage, Receptive Field Block Net (RFB Net) for more “human” receptive fields, BiseNet for lightweight segmentation, DeepLab v3+, etc
2. 🏀 Interesting usage of 3D CNNs to label NBA videos
Labelling NBA actions using deep learning. Label NBA broadcast footage with play-by-play descriptions, using 3D ConvNet-based video classification.
3. Deep Exemplar-based Colorization paper implementation
Deep Exemplar-based Colorization is the first deep learning approach for exemplar-based local colorization. Given a reference color image, our convolutional neural network directly maps a grayscale image to an output colorized image.
Paper: https://arxiv.org/pdf/1807.06587.pdf
4. Playing Mortal Kombat with your webcam in a the browser
Transfer learning, data augmentation, MobileNet, tensorflow.js, Mortal Kombat, webcam… It is a really fun project worth taking a look at
5. Object Detection status summary
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
https://arxiv.org/pdf/1809.03193.pdf
Machine Learning
6. Deep Learning on Collaborative Filtering at Hulu
Applying Deep Learning to Collaborative Filtering: How Hulu builds its industry leading recommendation engine using Neural Autoregressive Distribution Estimator for Collaborative filtering (CF-NADE). Will be nice to see how they adapt CF-NADE to implicit feedback in the future.
General Tools
7. When you git a jupyter notebook and then want to diff changes… 💣
Tool for diffing and merging of Jupyter Notebooks. Provides “content-aware” diffing and merging of Jupyter notebooks. It understands the structure of notebook documents. Therefore, it can make intelligent decisions when diffing and merging notebooks.
Recommender systems
8. Collaborative Embeddings for Lipstick Recommendations
Exploiting a nice idea that is normally over our heads: generating embeddings out of items in the inventory. Also, we appreciate that the author accounted for discoverability, factor that is typically ignored in recommender systems applications. Worth a couple of reads.
9. Applying Deep Learning To Airbnb Search
Remarkable paper from Airbnb making use of Deep Learning models in their business. Not only they tell the successful creations but also the unsuccessful atempts. Hashing tricks for high cardinality categoricals, rethinking the entire system surrounding the model when moving to deep learning, protobuf, Spark, multi-task learning, … joy.
https://arxiv.org/pdf/1810.09591.pdf
Graph Networks
10. Graph Nets library for TensorFlow
Build Graph Nets in Tensorflow. A graph network takes a graph as input and returns a graph as output. The input graph has edge- (E ), node- (V ), and global-level (u) attributes. The output graph has the same structure, but updated attributes.
And this is it for what we found out to be interesting during October. At xplore.ai, we are always trying out the latest tools, experimenting with cutting edge algorithms and reading about the latest trends in every industry where data is generating unprecedented value.
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