Distinguishing cause from effect using observational data:

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X; Y . This was often considered to be impossible. Nevertheless, several approaches for addressing this bivariate causal discovery problem were proposed recently. In this paper, we present the benchmark data set CauseEffectPairs that consists of 88 di erent \causee ect pairs’ selected from 31 datasets from various domains. We evaluated the performance of several bivariate causal discovery methods on these real-world benchmark data and on arti cially simulated data. Our empirical results provide evidence that additive-noise methods are indeed able to distinguish cause from e ect using only purely observational data. In addition, we prove consistency of the additive-noise method proposed by Hoyer et al. (2009).

What are the main differences between Granger’s and Pearl’s causality frameworks?

Recently, I ran across several papers and online resources that mention Granger causality. Brief browsing through the corresponding Wikipedia article left me with the impression that this term refers to causality in the context of time series (or, more generally, stochastic processes). Moreover, reading this nice blog post created an additional confusion in how to view this approach. I’m by no means a person knowledgeable about causality, as my fuzzy understanding of the concept consists of partly common sense, common knowledge, some exposure to latent variable modeling and structural equation modeling (SEM) and reading a bit from Judea Pearl’s work on causality – not THE book of his, but more along the lines of an interesting overview paper by Pearl (2009), which for some reason, surprisingly, doesn’t mention Granger causality at all. In this context, I’m wondering about whether Granger causality is something more general than a time series (stochastic) framework and, if such, what is its relation (commonalities and differences) to Pearl’s causality framework, based on the structural causal model (SCM), which, as far as I understand, is, in turn, based on direct acyclic graphs (DAGs) and counterfactuals. It seems that Granger causality can be classified as a general approach to causal inference for dynamic systems, considering the existence of dynamic causal modeling (DCM) approach (Chicharro & Panzeri, 2014). However, my concern is about whether (and, if so, how) it is possible to compare the two approaches, one of which is based on stochastic process analysis and the other is not.

Automated Workload Management Using Machine Learning

Mainframe System processing includes a ‘Batch Cycle’ that approximately spans 8 pm to 8 am, every week, from Monday night to Saturday morning. The core part of the cycle completes around 2 am, with key client deliverables associated with the end times of certain jobs, tracked by Service Delivery. There are single and multi-client batch streams, a QA stream which includes all clients, and about 2,00,000 batch jobs per day that execute. Despite a sophisticated job scheduling software, and automated system workload management, operator intervention is required, or believed to be required, to reprioritize when and what jobs get available system resources. Our work is to characterize, analyse and visualize the reasons for a manual change in the schedule. The work requires extensive data preprocessing and building machine learning models for the causal relationship between various system variables and the time of manual changes.

Real-Time Big Data Analysis Architecture and Application

Real-Time Big Data Analysis systems are those systems that process big data in given deadline or time limit. These types of systems are used to analysis a big data that is using data from some real world environment to analysis, predicate the solution to real-world problem. In this paper, we deal with architecture of this type of system what is basic structure of this type of system and their application in different area. We also categories theses type of in two main categories real-time system and near real-time system.

Novel Outlier Detection by Integration of Clustering and Classification

A unique method of outlier detection consisting of integration of clustering and classification is proposed here. Basically the algorithm is divided into two parts the first phase consists of application of the classical DBSCAN algorithm to the data set which is followed by the second phase which consists of application of decision tree classification algorithm. The analysis on the algorithm states that the accuracy of unwanted data detection is high in the proposed method.

Research Issue in Data Anonymization in Electronic Health Service: A Survey

At today time, the rapid change of technology is changing the day-to-day activity of human being. Healthcare data and practice also made use of these technologies; they change its way to handle the data. The electronic health Service (EHS) is increasingly collecting large amount of sensitive data of the patient that is used by the patient, doctors and others data analysts. When we are using EHS we should concern to security and privacy of the medical data, because of medical data is too sensitive due to their personal nature. Especially privacy is critical for the sensitive data when we give for medical data analysis or medical research purpose, first we should do sanitization or anonymized of the data before releasing it. Data anonymization is the removing or hiding of personal identifier information like name, id, and SSN from the health datasets and to not to be identified by the recipient of the data. To anonymize the data we are using different models and techniques of anonymization. This paper is survey on data anonymization in Electronic Health Service (EHS).

Text Classification Models with Tensorflow

Tensorflow implementation of Text Classification Models.
1. Word-level CNN
2. Character-level CNN
3. Very Deep CNN
4. Word-level Bidirectional RNN
5. Attention-Based Bidirectional RNN

What deep learning papers should I implement to learn?

• AlexNet: https://…n-with-deep-convolutional-neural-networks
• ZFNet: https://…/1311.2901
• VGG16: https://…/1505.06798
• ResNet: https://…/1704.06904
• GoogLeNet: https://…/1409.4842
• Inception: https://…/1512.00567
• Xception: https://…/1610.02357
• MobileNet: https://…/1704.04861
Semantic Segmentation
• FCN: https://…/1411.4038
• SegNet: https://…/1511.00561
• UNet: https://…/1505.04597
• PSPNet: https://…/1612.01105
• DeepLab: https://…/1606.00915
• ICNet: https://…/1704.08545
• ENet: https://…/1606.02147
Generative adversarial networks
• GAN: https://…/1406.2661
• DCGAN: https://…/1511.06434
• WGAN: https://…/1701.07875
• Pix2Pix: https://…/1611.07004
• CycleGAN: https://…/1703.10593
Object detection
• RCNN: https://…/1311.2524
• Fast-RCNN: https://…/1504.08083
• Faster-RCNN: https://…/1506.01497
• SSD: https://…/1512.02325
• YOLO: https://…/1506.02640
• YOLO9000: https://…/1612.08242

Empowering businesses and developers to do more with AI

Today, we´re sharing news on a number of new products and enhancement
• Cloud AutoML Vision, Natural Language, and Translation, available now in beta
• New enhancements to Dialogflow Enterprise Edition, available now in beta
• A new solution, Contact Center AI, available now in alpha

Basic Statistics in Python: Descriptive Statistics

The field of statistics is often misunderstood, but it plays an essential role in our everyday lives. Statistics, done correctly, allows us to extract knowledge from the vague, complex, and difficult real world. Wielded incorrectly, statistics can be used to harm and mislead. A clear understanding of statistics and the meanings of various statistical measures is important to distinguishing between truth and misdirection.

Selecting the best Machine Learning algorithm for your regression problem

When approaching any type of Machine Learning (ML) problem there are many different algorithms to choose from. In machine learning, there´s something called the ‘No Free Lunch’ theorem which basically states that no one ML algorithm is best for all problems. The performance of different ML algorithms strongly depends on the size and structure of your data. Thus, the correct choice of algorithm often remains unclear unless we test out our algorithms directly through plain old trial and error. But, there are some pros and cons to each ML algorithm that we can use as guidance. Although one algorithm won´t always be better than another, there are some properties of each algorithm that we can use as a guide in selecting the correct one quickly and tuning hyper parameters. We´re going to take a look at a few prominent ML algorithms for regression problems and set guidelines for when to use them based on their strengths and weaknesses. This post should then serve as a great aid in selecting the best ML algorithm for you regression problem!

Exploratory Data Analysis in R (introduction)

In this post we will review some functions that lead us to the analysis of the first case.
Step 1 – First approach to data
Step 2 – Analyzing categorical variables
Step 3 – Analyzing numerical variables
Step 4 – Analyzing numerical and categorical at the same time
Covering some key points in a basic EDA:
• Data types
• Outliers
• Missing values
• Distributions (numerically and graphically) for both, numerical and categorical variables.

Beyond Basic R – Data Munging

In this example, a data user is interested in how nutrient concentrations are related to discharge in a stream within a mixed use watershed of urban and agricultural land use. The site is located in Dane County near Madison, WI, and we´re focusing on daily values from a 20-year period from 1998 to 2017. We´ll use these data to demonstrate a variety of data munging techniques that are beyond the basics.

How privacy-preserving techniques can lead to more robust machine learning models

What about machine learning? While I didn´t have space to point this out in my previous post, differential privacy has been an area of interest to many machine learning researchers. Most practicing data scientists aren´t aware of the research results, and popular data science tools haven´t incorporated differential privacy in meaningful ways (if at all). But things will change over the next months. For example, Liu wants to make ideas from differential privacy accessible to industrial data scientists, and she is part of a team building tools to make this happen.

Differential Privacy and Machine Learning: a Survey and Review

The objective of machine learning is to extract useful information from data, while privacy is preserved by concealing information. Thus it seems hard to reconcile these competing interests. However, they frequently must be balanced when mining sensitive data. For example, medical research represents an important application where it is necessary both to extract useful information and protect patient privacy. One way to resolve the conflict is to extract general characteristics of whole populations without disclosing the private information of individuals. In this paper, we consider differential privacy, one of the most popular and powerful definitions of privacy. We explore the interplay between machine learning and differential privacy, namely privacy-preserving machine learning algorithms and learning-based data release mechanisms. We also describe some theoretical results that address what can be learned differentially privately and upper bounds of loss functions for differentially private algorithms. Finally, we present some open questions, including how to incorporate public data, how to deal with missing data in private datasets, and whether, as the number of observed samples grows arbitrarily large, differentially private machine learning algorithms can be achieved at no cost to utility as compared to corresponding non-differentially private algorithms.