We define and study error detection and correction tasks that are useful for 3D reconstruction of neurons from electron microscopic imagery, and for image segmentation more generally. Both tasks take as input the raw image and a binary mask representing a candidate object. For the error detection task, the desired output is a map of split and merge errors in the object. For the error correction task, the desired output is the true object. We call this object mask pruning, because the candidate object mask is assumed to be a superset of the true object.
In 2016, the computer program AlphaGo won four out of five games of Go against the world's best human player. Given that a game of Go has more combinations of moves than there are estimated to be atoms in the universe, this required more than just sheer processing power. Rather, AlphaGo used artificial neural networks, which can recognize visual patterns and are even capable of learning. Unlike a human, the program was able to practise hundreds of thousands of games in a short time, eventually surpassing the best human player. Now, the Erlangen-based researchers are using neural networks of this kind to develop error-correction learning for a quantum computer.
Current grammatical error correction (GEC) models typical ly consider the task as sequence generation, which requires la rge amounts of annotated data and limit the applications in data - limited settings. We try to incorporate contextual informa tion from pre-trained language model to leverage annotation and benefit multilingual scenarios. Results show strong potent ial of Bidirectional Encoder Representations from Transforme rs (BERT) in grammatical error correction task.
January 17, 2017 --The dream of useful quantum computing may have just come one step closer. Australian researchers are combining two of the hottest topics in science: quantum computing and machine learning. Specifically, they've succeeded in training an algorithm to predict the evolving state of a simple quantum computer. Such an understanding allows real time stabilization of the system, much as tightrope walker uses a pole for balance, according to a paper published Monday in Nature Communications. That would be a big deal for everyone – from Silicon Valley to Washington, D.C.
Correcting recognition errors is often necessary in a speech interface. These errors not only reduce users' overall entry rate, but can also lead to frustration. While making fewer recognition errors is undoubtedly helpful, facilities for supporting user-guided correction are also critical. We explore how to better support user corrections using Parakeet — a continuous speech recognition system for mobile touch-screen devices. Parakeet's interface is designed for easy error correction on a handheld device. Users correct errors by selecting alternative words from a word confusion network and by typing on a predictive software keyboard. Our interface design was guided by computational experiments and used a variety of information sources to aid the correction process. In user studies, participants were able to write text effectively despite sometimes high initial recognition error rates. Using Parakeet as an example, we discuss principles we found were important for building an effective speech correction interface.