Four short links: 8 May 2017
Skimming Text, Image Attribute Transfer, Reproducible Research, and Robots Surviving Clutter
- Learning to Skim Text — Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. Basically implementing a teenager reading for school, then. (via hardmaru on Twitter)
- Visual Attribute Transfer through Deep Image Analogy — the sample images are stunning.
- Practice of Reproducible Research — 31 case studies of reproducible research workflows, written by academic researchers in the data-intensive sciences. Each case study describes how the author combined specific tools, ideas, and practices in order to complete a real-world research project. Emphasis is placed on the practical aspects of how the author organized his or her research to make it as reproducible as possible.
- Manipulation Under Clutter and Uncertainty With And Around People (YouTube) — an hour-long lecture on the challenges of robotics in actual human environments. I hope they take a while to solve this problem, because hiding in my kid’s room is literally my only survival plan for the robopocalypse.
