DeepTracker: Visualizing the Training Process of Convolutional Neural Networks

Puppet

An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)

Can we infer intentions and goals from a person’s actions? As an example of this family of problems, we consider here whether it is possible to decipher what a person is searching for by decoding their eye movement behavior. We conducted two human psychophysics experiments on object arrays and natural images where we monitored subjects’ eye movements while they were looking for a target object. Using as input the pattern of ‘error’ fixations on non-target objects before the target was found, we developed a model (InferNet) whose goal was to infer what the target was. ‘Error’ fixations share similar features with the sought target. The Infernet model uses a pre-trained 2D convolutional architecture to extract features from the error fixations and computes a 2D similarity map between the error fixation and all locations across the search image by modulating the search image via convolution across layers. InferNet consolidates the modulated response maps across layers via max pooling to keep track of the sub-patterns highly similar to features at error fixations and integrates these maps across all error fixations. InferNet successfully identifies the subject’s goal and outperforms all the competitive null models, even without any object-specific training on the inference task. What am I searching for?

The videos from the NYC R conference have been published, and there are so many great talks there to explore. I highly recommend checking them out: you’ll find a wealth of interesting R applications, informative deep dives on using R (and a few other applications as well), and some very entertaining deliveries. In this post, I wanted to highlight a couple of talks in particular. 

Maciej Cegłowski writes:

I participated in Santander Customer Satisfaction challenge, ran on Kaggle for 2 months and got into top 1%. Here, I would be discussing my approach to this problem.

Despite numerous cybersecurity efforts, phishing attacks are still on the rise. Phishing is a form of fraud where perpetrators pretend to be reputable companies and attempt to get individuals to reveal personal information, such as passwords and credit card numbers. It’s the most common social tactic.  93 percent of all breaches today start with phishing emails, according to a recent Verizon Data Breach Investigations Report.

Alex Konkel writes on a topic that never goes out of style:

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications. We’re excited to announce the availability of a new feature called Channel Identification, which allows users to process multi-channel audio files and retrieve a single transcript annotated with respective channel labels.