Conditional Sequential Modulation for Efficient Global Image Retouching

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations… In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework – Conditional Sequential Retouching Network (CSRNet) – for […]

September 25, 2020 Machine Learning, RELATION EXTRACTION

Global-to-Local Neural Networks for Document-Level Relation Extraction

Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document… In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations. Entity global representations model the semantic information of all entities in the document, entity […]

September 25, 2020 Machine Learning

LP2PB: Translating Answer Set Programs into Pseudo-Boolean Theories

Answer set programming (ASP) is a well-established knowledge representation formalism. Most ASP solvers are based on (extensions of) technology from Boolean satisfiability solving… While these solvers have shown to be very successful in many practical applications, their strength is limited by their underlying proof system, resolution. In this paper, we present a new tool LP2PB that translates ASP programs into pseudo-Boolean theories, for which solvers based on the (stronger) cutting plane proof system exist. We evaluate our tool, and the […]

September 25, 2020 Data

Python Data Tools

Python is an increasingly popular object-oriented, interpreted and interactive programming language used for heavy-duty data analysis. Python is designed for ease-of-use, speed, readability and tailored for data-intensive applications. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming styles. It features a fully dynamic type system and automatic memory management, similar to that of Scheme, Ruby, Perl and Tcl. You can create customized data tools using Python that can handle large data sets efficiently – it lets you work more quickly and […]

September 25, 2020 Analysis

An indispensable Python : Data sourcing to Data science.

Data analysis echo system has grown all the way from SQL’s to NoSQL and from Excel analysis to Visualization. Today, we are in scarceness of the resources to process ALL (You better understand what i mean by ALL) kind of data that is coming to enterprise. Data goes through profiling, formatting, munging or cleansing, pruning, transformation steps to analytics and predictive modeling. Interestingly, there is no one tool proved to be an effective solution to run all these operations { Don’t forget the […]

September 25, 2020 Algorithm

Random Forests Algorithm

One of the most popular methods or frameworks used by data scientists at the Rose Data Science Professional Practice Group is Random Forests. The Random Forests algorithm is one of the best among classification algorithms – able to classify large amounts of data with accuracy. Random Forests are an ensemble learning method (also thought of as a form of nearest neighbor predictor) for classification and regression that construct a number of decision trees at training time and outputting the class that is […]

September 25, 2020 Analytics

Python Scikit-learn to simplify Machine learning : { Bag of words } To [ TF-IDF ]

Text (word) analysis and tokenized text modeling always give a chill air around ears, specially when you are new to machine learning. Thanks to Python and its extended libraries for its warm support around text analytics and machine learning. Scikit-learn is a savior and excellent support in text processing when you also understand some of the concept like “Bag of word”, “Clustering” and “vectorization”. Vectorization is must-to-know technique for all machine leaning learners, text miner and algorithm implementor. I personally consider […]

September 25, 2020 Arduino

Plotly Beta: Graphing and Analytics Platform

Hey Data Scientists, I wanted to reach out about Plot.ly, a new startup for analyzing and beautifully visualizing data. We just launched a beta. It is built for math, science, and data applications. We’d love your thoughts. Overview: You can import data from anywhere, and analyze it in our grid with stats, fits, functions, and more. Our plotting APIs (R, Python, MATLAB, Arduino, REST, Julia, Perl) and grid make interactive, web-ready, publication-quality graphs. We have a Python Shell, and interactive graphs […]

September 25, 2020 Charts

New in Plotly: Interactive Graphs with IPython

New! Plotly lets you style interactive graphs in IPython. Then, you can share your Notebook or your Plotly graph. It’s like having the NYTimes graphics department inside your IPython. You can also get these Notebooks on the Plotly GitHub page. Visit Plot.ly to see more documentation. Here’s a preview of how it looks to have your code, data, and graph all interactively available. See the live version. To finish reading, please visit source site

September 25, 2020 Python

Data Science – learn R or Python?

Hi Folks, I have a query around whether to learn R from scratch or should I leverage my basic python knowledge to extend into Data Science with scikit,numpy ,pandas? So I am bit confused … I am not shy to learn New programming language like R etc bur really need to know who edges out whom in market. Maybe i should learn R too along with Python so your valuable opinion matters. Also i […]

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