Popular GitHub Repositories for Preprocessed Observations

RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code

Preprocessed Python functions and docstrings for automated code documentation (code2doc) and automated code generation (doc2code) tasks.

Observations and notes to understand the workings of neural network models and other thought experiments using Tensorflow

Learn how to train a quadruped robot to walk using reinforcement learning, from defining actions and observations to designing rewards and transitioning from simulation to reality.

[NeurIPS 2020, Spotlight] Code for "Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations"

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

AutoRF: Learning 3D Object Radiance Fields from Single View Observations (CVPR 2022)

Modified versions of the SAC algorithm from spinningup for discrete action spaces and image observations.

Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022

Official repository for the paper "Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations" (NeurIPS 2022)

[ICCV 2023] Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling

This repository contains resources for accessing the official benchmarks, codes, and checkpoints of the paper: "[**Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations**]".

Code for: "Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs", ICCV2021 Workshops

Learning Diffusion Priors from Observations by Expectation Maximization

An in-development R package and a Bayesian hierarchical model jointly fitting multiple "local" wastewater data streams and "global" case count data to produce nowcasts and forecasts of both observations

We created a topic modeling pipeline to evaluate different topic modeling algorithms, including their performance on short and long text, preprocessed and not preprocessed datasets, and with different embedding models. Finally, we summarized the results and suggested how to choose algorithms based on the task.

SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations

This is a repo to implement Anomaly Detection which is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations.

A python package for finding causal functional connectivity from neural time series observations.

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.