October 16, 2021 Bayesian

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires the following packages: sobol_seq platypus sklearn.gaussian_process Citation If you use this code in your academic work please cite our JAIR paper: “A General Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization ” and our workshop paper “Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations” , Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa. GitHub – belakaria/iMOCA at pythonawesome.com Contribute to belakaria/iMOCA development by creating an […]

October 16, 2021 Images

Python implementation of image filters (such as brightness, contrast, saturation, etc.)

Python implementation of image filters Use Python to adjust brightness and contrast, add blur, and detect edges! Follow along tutorial: https://youtu.be/4ifdUQmZqhM In order to download this code, either click the green button at the top right and download as ZIP, or use git clone https://github.com/kying18/pyphotoshop.git. You will need to pip install -r requirements.txt (or use pip3 if you are getting a module not found error). In the folder, you will find these files: image.py: contains the Image class that will […]

October 16, 2021 Neural Network

An Open-Source Package for Neural Relation Extraction (NRE)

We have a DEMO website (http://opennre.thunlp.ai/). Try it out! OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement relation extraction models. This package is designed for the following groups: New to relation extraction: We have hand-by-hand tutorials and detailed documents that can not only enable you to use relation extraction tools, but also help you better understand the research progress in this field. Developers: Our easy-to-use interface and high-performance implementation can acclerate your deployment in […]

October 16, 2021 Text

Unsupervised text tokenizer focused on computational efficiency

YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE) [Sennrich et al.]. Our implementation is much faster in training and tokenization than Hugging Face, fastBPE and SentencePiece. In some test cases, it is 90 times faster. Check out our benchmark results. Key advantages: Multithreading for training and tokenization The algorithm has O(N) complexity, where N is the length of training data Highly efficient implementation in C++ Python wrapper and command-line interface […]

October 16, 2021 Deep Learning

A deep learning based natural language and speech processing platform

What is DELTA? DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper. What can DELTA do? DELTA has been used for developing several state-of-the-art algorithms for publications and

October 16, 2021 Python

Pip vs Conda: an in-depth comparison of Python’s two packaging systems

If you’re using Python in the world of data science or scientific computing, you will soon discover that Python has two different packaging systems: pip and Conda. Which raises some questions: How are they different? What are the tradeoffs between the two? Which should you use? While it’s not possible to answer this question for every situation, in this article you will learn the basic differences, constrained to: Python only; Conda has support for other languages but I won’t go […]

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