Deep learning has revolutionized the machine understanding of imagery. Yet today's image recognition models are still limited by the availability of large annotated training datasets upon which to build their libraries of recognized objects and activities. To address this, Google's Vision AI API expands its native catalog of around 10,000 visually recognized objects and activities with the ability to perform the equivalent of a reverse Google Images search across the open Web and tally up the top topics used to caption the given image everywhere it has previously appeared, lending unprecedentedly rich context and understanding, even yielding unique labels for breaking news events. What might this process yield for a week of television news? Google's Vision AI API represents a unique hybrid between traditional deep learning-based image labeling based on a library of previously trained models and the ability to leverage the open Web to annotate images based on the most common topics visually similar images are captioned with. Using its Web Entities feature, the Vision AI API performs what amounts to a reverse Google Images search over the open Web, identifying images across the entire Web that look most similar to the given image.

Discriminative pattern mining is an essential task of data mining. This task aims to discover patterns which occur more frequently in a class than other classes in a class-labeled dataset. This type of patterns is valuable in various domains such as bioinformatics, data classification. In this paper, we propose a novel algorithm, named SSDPS, to discover patterns in two-class datasets. The SSDPS algorithm owes its efficiency to an original enumeration strategy of the patterns, which allows to exploit some degrees of anti-monotonicity on the measures of discriminance and statistical significance. Experimental results demonstrate that the performance of the SSDPS algorithm is better than others. In addition, the number of generated patterns is much less than the number of the other algorithms. Experiment on real data also shows that SSDPS efficiently detects multiple SNPs combinations in genetic data.

In part 1 of this series, we gave our bot the ability to detect sentiment from text and respond accordingly. But that's about all it can do, and admittedly quite boring. Of course, in a real chat, we often send a multitude of media: from text, images, videos, gifs, to anything else. So in this, our next step in our journey, let's give our bot vision. The goal of this tutorial is to allow our bot to receive images, reply to them, and eventually give us a crude description of the main object in said image.

In part 1 of this series, we gave our bot the ability to detect sentiment from text and respond accordingly. But that's about all it can do, and admittedly quite boring. Of course, in a real chat, we often send a multitude of media: from text, images, videos, gifs, to anything else. So in this, our next step in our journey, let's give our bot vision. The goal of this tutorial is to allow our bot to receive images, reply to them, and eventually give us a crude description of the main object in said image.

Satisfying the consumers is the most important thing of any business advancements and that's what we helped our beverage partner achieve using Machine Learning. Image recognition is widely being accepted in the FMCG sector to eliminate poor management, supply and storage-related issues of the products. We are living in a mobile era relying heavily on apps for every other thing from ordering groceries to professional work to booking cabs and tickets. Still, for participating in contests, consumers have to manually send SMS for making their entries. What if there's a mobile or web app which is fast, small, accurate and can remove this manual work of sending SMS just by scanning an image!

Hey! I'm new to this community and also I'm working on a project that might have a good scenario to implement text recognition in order to output images for them. Please, if anyone knows where I could start or which tools I could try it would be great!

This paper introduces an approach for detecting differences in the first-order structures of spatial point patterns. The proposed approach leverages the kernel mean embedding in a novel way by introducing its approximate version tailored to spatial point processes. While the original embedding is infinite-dimensional and implicit, our approximate embedding is finite-dimensional and comes with explicit closed-form formulas. With its help we reduce the pattern comparison problem to the comparison of means in the Euclidean space. Hypothesis testing is based on conducting $t$-tests on each dimension of the embedding and combining the resulting $p$-values using the harmonic mean $p$-value combination technique. The main advantages of the proposed approach are that it can be applied to both single and replicated pattern comparisons, and that neither bootstrap nor permutation procedures are needed to obtain or calibrate the $p$-values. Our experiments show that the resulting tests are powerful and the $p$-values are well-calibrated; two applications to real world data are presented.

This paper introduces the Contextual Evaluation Model (CEM), a novel method for knowledge representation and manipulation. The CEM differs from existing models in that it integrates facts, patterns and sequences into a single contextual framework. V5, an implementation of the model is presented and demonstrated with multiple annotated examples. The paper includes simulations demonstrating how the model reacts to pleasure/pain stimuli. The 'thought' is defined within the model and examples are given converting thoughts to language, converting language to thoughts and how 'meaning' arises from thoughts. A pattern learning algorithm is described. The algorithm is applied to multiple problems ranging from recognizing a voice to the autonomous learning of a simplified natural language.

Deep learning (DL) has solved a problem that as little as five years ago was thought by many to be intractable - the automatic recognition of patterns in data; and it can do so with accuracy that often surpasses human beings. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners trying to make sense out of the flood of data that now inundates our society. As public awareness of the efficacy of DL increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge produced by experts in the field. Where does one start? How does one determine if a particular model is applicable to their problem? How does one train and deploy such a network? A primer on the subject can be a good place to start. With that in mind, we present an overview of some of the key multilayer ANNs that comprise DL. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is becoming critical to many computer applications, we include a section on using neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where DL has emerged as a game-changing technology: anomalous behavior detection in financial applications or in financial time-series forecasting, predictive and prescriptive analytics, medical image processing and analysis and power systems research. The thrust of this review is to outline emerging areas of application-oriented research within the DL community as well as to provide a reference to researchers seeking to use it in their work for what it does best: statistical pattern recognition with unparalleled learning capacity with the ability to scale with information.

In this paper, we present a new dataset for Form Understanding in Noisy Scanned Documents (FUNSD). Form Understanding (FoUn) aims at extracting and structuring the textual content of forms. The dataset comprises 200 fully annotated real scanned forms. The documents are noisy and exhibit large variabilities in their representation making FoUn a challenging task. The proposed dataset can be used for various tasks including text detection, optical character recognition (OCR), spatial layout analysis and entity labeling/linking. To the best of our knowledge this is the first publicly available dataset with comprehensive annotations addressing the FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset. The FUNSD dataset can be downloaded at https://guillaumejaume.github. io/FUNSD/.