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ML Digest: LSTMs on Brainwave Data
Welcome to this week of the Best of Machine Learning Digest. In this weekly newsletter, we resurface some of the best resources in Machine Learning posted in the past week. This time, we've gotten 42 submissions, including 2 papers.
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Papers

This week, 2 Papers were posted on Best of ML. In the following, we're showing you the Top 2 posts of this week.
DeepBrain: Towards Personalized EEG Interaction through Attentional and Embedded LSTM Learning
 
The "mind-controlling" capability has always been in mankind's fantasy. With the recent advancements of electroencephalograph (EEG) techniques, brain-computer interface (BCI) researchers have explored various solutions to allow individuals to perform various tasks using their minds. However, the commercial off-the-shelf devices to run accurate EGG signal collection are usually expensive and the comparably cheaper devices can only present coarse results, which prevents the practical application of these devices in domestic services. To tackle this challenge, we propose and develop an end-to-end solution that enables fine brain-robot interaction (BRI) through embedded learning of coarse EEG signals from the low-cost devices, namely DeepBrain, so that people having difficulty to move, such as the elderly, can mildly command and control a robot to perform some basic household tasks. Our contributions are two folds: 1) We present a stacked long short term memory (Stacked LSTM) structure with specific pre-processing techniques to handle the time-dependency of EEG signals and their classification. 2) We propose personalized design to capture multiple features and achieve accurate recognition of individual EEG signals by enhancing the signal interpretation of Stacked LSTM with attention mechanism. Our real-world experiments demonstrate that the proposed end-to-end solution with low cost can achieve satisfactory run-time speed, accuracy and energy-efficiency.
 
Self-Supervised Linear Motion Deblurring
 
Motion blurry images challenge many computer vision algorithms, e.g, feature detection, motion estimation, or object recognition. Deep convolutional neural networks are state-of-the-art for image deblurring. However, obtaining training data with corresponding sharp and blurry image pairs can be difficult. In this paper, we present a differentiable reblur model for self-supervised motion deblurring, which enables the network to learn from real-world blurry image sequences without relying on sharp images for supervision. Our key insight is that motion cues obtained from consecutive images yield sufficient information to inform the deblurring task. We therefore formulate deblurring as an inverse rendering problem, taking into account the physical image formation process: we first predict two deblurred images from which we estimate the corresponding optical flow. Using these predictions, we re-render the blurred images and minimize the difference with respect to the original blurry inputs. We use both synthetic and real dataset for experimental evaluations. Our experiments demonstrate that self-supervised single image deblurring is really feasible and leads to visually compelling results.
 

Blog Posts

This week, 25 Blog Posts were posted on Best of ML. In the following, we're showing you the Top 3 posts of this week.
Cost-Sensitive Learning for Imbalanced Classification
 
Most machine learning algorithms assume that all misclassification errors made by a model are equal. This is often not the case for imbalanced classification problems where missing a positive or minority class case is worse than incorrectly classifying an example from the negative or majority class. There are many real-world examples, such as detecting spam email, diagnosing a medical condition, or identifying fraud. In all of these cases, a false negative (missing a case) is worse or more costly than a false positive.
 
How to Detect Anomalies in Healthcare Using Machine Learning
 
The digital revolution has changed the healthcare landscape irrevocably. Patients now expect faster, more efficient care that costs less. Which is where artificial intelligence (AI) can help. AI and machine learning allow healthcare organizations to evolve and keep up with trends and new methodologies.
 
DBSCAN Clustering for Trading
 
Pioneered in the 80’s by quantitative analysts at Morgan Stanley, pairs trading is a trading strategy that allows traders to profit in almost any market conditions. This strategy involves monitoring two historically correlated securities. Once a correlation discrepancy such as one stock going up and the other remaining stagnant is identified, the investor goes long on the underperforming security and short on the outperforming security, hoping that the securities return to their historical correlation. In the case that this does happen, the investor makes a profit from the convergence of the prices.
 






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