fmpr/Combining-TimeSeries-TextData

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Created over 6 years ago
Updated over 6 years ago

fmpr/Combining-TimeSeries-TextData

fmpr

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A deep learning approach for combining time-series and textual data for taxi demand prediction in event areas

CrowdLayer

A neural network layer that enables training of deep neural networks directly from crowdsourced labels (e.g. from Amazon Mechanical Turk) or, more generally, labels from multiple annotators with different biases and levels of expertise.

Jupyter Notebook

texttk

Text Preprocessing in Python

DeepJMQR

Deep joint mean and quantile regression for spatio-temporal problems

Jupyter Notebook

CAREL

CAREL is open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment.

pyDCML

PyDCML is a Python library for fast implementation and scalable inference of Bayesian Discrete Choice Models that makes it easy to leverage flexible state-of-the-art modelling techniques from Machine Learning, while remaining interpretable and preserving the links with economic theories.

Jupyter Notebook

mobility-baselines

Baselines and benchmarks for spatio-temporal forecasting problems in transportation.

Jupyter Notebook

MA-sLDAc

Multi-Annotator Supervised LDA for classification

amortized-mxl

Amortized-MXL: Scaling Bayesian Inference of Mixed Multinomial Logit Models to Very Large Datasets

Jupyter Notebook

LogReg-Crowds

Logistic Regression from Crowds

MA-sLDAr

Multi-Annotator Supervised LDA for regression

DCM-ARD

A Bayesian ARD approach for automatically determining an optimal utility function specification in Discrete Choice Models (DCMs) from an exponentially large set of possible specifications in a purely data-driven manner.