15 curated AI reads for September 2018
September, 2018
News about Machine Learning (ML), Artificial Intelligence (AI), Data Science (DS) and related advanced analytics areas.
Welcome to xplore.ai’s third post of the 15 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.
15. ✏️ Academic Torrents
A scalable, secure, and fault-tolerant repository for data, with blazing fast download speeds. We are thankful for such work, kudos to Lo, Henry Z. and Cohen, Joseph P., for such a big effort.
14. 💃 DanceNet
Dance generator using Autoencoders, LSTM and Mixture Density Network. Build in Keras. Paper and code clicking.
https://raw.githubusercontent.com/ft-interactive/chart-doctor/master/visual-vocabulary/poster.png
13. 💣AutoKeras
A novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search by introducing a neural network kernel and a tree-structured acquisition function optimization algorithm. With Bayesian optimization to select the network morphism operations, the exploration of the search space is more efficient
12. 🏠 Creating 3D renders out of a single image of a space
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image using a stochastic grammar model integrated with latent human context, geometry and physics. Paper here. Pretty cool job.
11. 🖇️️ Adversarial Vision Challenge at NIPS 2018
“The overall goal of this challenge is to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. As of right now, modern machine vision algorithms are extremely susceptible to small and almost imperceptible perturbations of their inputs (so-called adversarial examples). This property reveals an astonishing difference in the information processing of humans and machines and raises security concerns.”.
10. 📙Model-based ML book — Early access
Applied ML book, early access. “In this book we look at machine learning from a fresh perspective which we call model-based machine learning. This viewpoint helps to address all of these challenges, and makes the process of creating effective machine learning solutions much more systematic.”
9. 💧Attentive Generative Adversarial Network for Raindrop Removal from A Single Image (CVPR’2018)
Very interesting Deep Learning project to remove raindrops from images. They used 1119 pairs of images, with various background scenes and raindrops, simulated with sprayed glasses. The other side of the pair is the same scene but without the glass with the drops. The method utilizes a generative adversarial network, where the generative network produces the attention map via an attentive-recurrent network and applies this map along with the input image to generate a raindrop-free image through a contextual autoencoder. The discriminative network then assesses the validity of the generated output globally and locally. To be able to validate locally, they inject the attention map into the network. The authors novelty lies on the use of the attention map in both generative and discriminative network..
8. 🔖 Learning Market Dynamics for Optimal Pricing (not only ML)
Strongly recommended (pure art) post from Engineering at AirBnB where they model the lead time probability of a booking. “This model is currently used in product, primarily to power their Smart Pricing. The tool uses the predicted lead time distributions for each check-in to help hosts keep prices up to date. We also use it to inform hosts about booking lead times statistics to help them make informed decisions around calendar availability”.
They combine 2 optimization functions: 1 time to event can be approximated by a gamma distribution because the bookings in a unit of time are Poisson distributed. The other function is the oscillating waveform adjuster that will multiply the 1st function. Click to keep reading:
7. 📘 State of the art text classification with universal language models
Fine tuning NLP models with ULMFiT. Full neural networks in practice contain many layers, so only using transfer learning for a single layer (word2vec) was clearly just scratching the surface of what’s possible. J Howard et al reached the performance of training from scratch on 100x more data.
6. ↩️ Hyperparameter tuning visual replay in Keras
Deep Replay — Generate visualizations as in my “Hyper-parameters in Action!” series! Replay in a visual fashion the training process of a Deep Learning model in Keras
5. 🇹 TensorFlow 2.0
We are glad to here that, among various changes, there’s a focus on the Eager execution mode, which will be a central feature.
4. 🕺 Vid2Vid
Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation. You can read the paper here. Goal is to map sequence of semantic segmentation masks to an output photorealistic video that precisely depicts the content of the source video.
3. 😀Time Series Forecasting Cheat Sheet
We have been waiting for something like this for a long time now. It was about time to have a comprehensible short document explaining the well-known Forecasting methods out there.
2. 📈 PolyRNN+
Tool for Efficient Annotation of Segmentation Datasets.
1. 🌱 Automatic answers to Q about an image
Visual Question Answering (VQA). Data: 82,783 training images from COCO (common objects in context) dataset + 443,757 question-answer pairs for training images + 40,504 validation images to perform own testing + 214,354 question-answer pairs for validation images. Model: 3 stacked 512-unit LSTM layers to process questions + VGG16 for the images.
And this is it for what we found out to be interesting during sunny August. 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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