September 26, 2021 Deep Learning

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

This paper is the code release of the paper GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition, which is accepted at EACL-2021. This work aims at improving weakly supervised named entity reconigtion systems by automatically finding new rules that are helpful at identifying entities from data. The idea is, as shown in the following figure, if we know rule1: associated with->Disease is an accurate rule and it is semantically related to rule2: cause of->Disease, we should be […]

September 26, 2021 ffmpeg

Run an FFmpeg command and see the percentage progress and ETA

A command line program that runs an FFmpeg command and shows the following in addition to the FFmpeg output: Percentage Progress Speed ETA (minutes and seconds) Example: Progress: 25% | Speed: 22.3x | ETA: 1m 33s python3 better_ffmpeg_progress.py -c “ffmpeg -i input.mp4 -c:a libmp3lame output.mp3” I have also included a function, which can be imported and used in your own Python program or script: run_ffmpeg_show_progress(“ffmpeg -i input.mp4 -c:a libmp3lame output.mp3”) GitHub https://github.com/CrypticSignal/better-ffmpeg-progress

September 26, 2021 Real Time

Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation

This repository contains the implementation of the following paper: Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation Yuanxun Lu, Jinxiang Chai, Xun Cao (SIGGRAPH Asia 2021) Abstract: To the best of our knowledge, we first present a live system that generates personalized photorealistic talking-head animation only driven by audio signals at over 30 fps. Our system contains three stages. The first stage is a deep neural network that extracts deep audio features along with a manifold projection to project the features […]

September 25, 2021 CNN

Auralisation of learned features in CNN (for audio)

This repo is for an example of auralisastion of CNNs that is demonstrated on ISMIR 2015. Files auralise.py: includes all required function for deconvolution. example.py: includes the whole code – just clone and run it by python example.py You might need to use older version of Keras, e.g. this (ver 0.3.x) Folders src_songs: includes three songs that I used in my blog posting. Usage Load weights that you want to auralise. I’m using this function W = load_weights() to load […]

September 25, 2021 Language Models

Interpretability and explainability of data and machine learning models

The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. […]

September 25, 2021 scikit-learn

Interpreting scikit-learn’s decision tree and random forest predictions

Package for interpreting scikit-learn’s decision tree and random forest predictions. Allows decomposing each prediction into bias and feature contribution components as described in http://blog.datadive.net/interpreting-random-forests/. For a dataset with n features, each prediction on the dataset is decomposed as prediction = bias + feature_1_contribution + … + feature_n_contribution. It works on scikit-learn’s DecisionTreeRegressor DecisionTreeClassifier ExtraTreeRegressor ExtraTreeClassifier RandomForestRegressor RandomForestClassifier ExtraTreesRegressor ExtraTreesClassifier Free software: BSD license Dependencies Installation The easiest way to install the package is via pip: $ pip install treeinterpreter Usage […]

September 25, 2021 Machine Learning

A library that implements fairness-aware machine learning algorithms

themis-ml is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms. themis-ml defines discrimination as the preference (bias) for or against a set of social groups that result in the unfair treatment of its members with respect to some outcome. It defines fairness as the inverse of discrimination, and in the context of a machine learning algorithm, this is measured by the degree to which the algorithm’s predictions favor one social group over another […]

September 25, 2021 Interpreter

Python Library for Model Interpretation/Explanations

Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The project was started […]

September 25, 2021 Plot

Python Individual Conditional Expectation Plot Toolbox

Python Individual Conditional Expectation Plot Toolbox A Python implementation of individual conditional expecation plots inspired by R’s ICEbox. Individual conditional expectation plots were introduced in Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation (arXiv:1309.6392). Quickstart pycebox is available on PyPI and can be installed with pip install pycebox. The tutorial recreates the first example in the above paper using pycebox. Development For easy development and prototyping using IPython notebooks, a Docker environment is included. […]

September 25, 2021 BreakDown

Python implementation of R package breakDown

Python implementation of breakDown package (https://github.com/pbiecek/breakDown). Docs: https://pybreakdown.readthedocs.io. Requirements Nothing fancy, just python 3.5.2+ and pip. Installation Install directly from github git clone https://github.com/bondyra/pyBreakDown cd ./pyBreakDown python3 setup.py install # (or use pip install . instead) Basic usage Load dataset from sklearn import datasets x = datasets.load_boston() feature_names = x.feature_names Prepare model model = tree.DecisionTreeRegressor() Train model

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