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bayesianlstm

Bayesian LSTM (Tensorflow)

cooperative_trajectory_prediction_under_occlusion

This repo presents an algorithm to estimate the state of an occluded object and probabilistically predict the future states using cooperative perception with depth cameras.

deep-learning-for-bci

Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications

dynamixel_fish

Servo control of fish fin undulation using servo

eeg-datasets

A list of all public EEG-datasets

gaussian-processes-regression-tutorial

An Intuitive Tutorial to Gaussian Processes Regression

important_papers

Summaries of papers on machine learning, computer vision, Bayesian Methods etc.

mpc_for_agv

This repository contains MPC code for control of Autonomous Ground Vehicle

mujoco-2.1-rl-project

Implementing Deep Reinforcement Learning Algorithms in Python for use in the MuJoCo Physics Simulator

opentraj

Human Trajectory Prediction Dataset Benchmark (ACCV 2020)

practical_rl

A course in reinforcement learning in the wild

probabilistic_ensemble_trajectory_sampling

Experiment code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"

pytorch-tutorial

Build your neural network easy and fast, 莫烦Python中文教学

ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

reinforcement-learning

rl

An advanced course on reinforcement learning offered at Columbia University IEOR in Spring 2018

trajectron-plus-plus

Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).

uncertainty-deep-learning

Monte Carlo Dropout, Deep Ensemble, Bayes-by-Back-Prop

uncertainty-estimation-using-bayesian-approximation

uncertaintyestimation

Uncertainty Estimation Using Deep Neural Network and Gradient Boosting Methods