xiaowang (onism)

onism

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Location:haerbin

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xiaowang's starred repositories

100-Days-Of-ML-Code

100 Days of ML Coding

License:MITStargazers:44045Issues:2441Issues:0

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

Language:Jupyter NotebookLicense:MITStargazers:7110Issues:374Issues:81

Deep-Learning-for-Tracking-and-Detection

Collection of papers, datasets, code and other resources for object tracking and detection using deep learning

deepbayes-2018

Seminars DeepBayes Summer School 2018

Language:Jupyter NotebookStargazers:1045Issues:79Issues:6

Complex-YOLO

Complex-YOLO: Real-time 3D Object Detection on Point Clouds pytorch Darknet

TrackerComponentLibrary

This is a collection of Matlab functions that are useful in the development of target tracking algorithms.

Language:MATLABLicense:NOASSERTIONStargazers:362Issues:44Issues:16

pyvarinf

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

Language:PythonLicense:MITStargazers:358Issues:11Issues:3

learning-to-reweight-examples

PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

Language:Jupyter NotebookStargazers:351Issues:12Issues:21

importance-sampling

Code for experiments regarding importance sampling for training neural networks

Language:PythonLicense:NOASSERTIONStargazers:319Issues:14Issues:35

3DOD_thesis

3D Object Detection for Autonomous Driving in PyTorch, trained on the KITTI dataset.

Language:PythonLicense:MITStargazers:282Issues:27Issues:10

segnet

A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Language:Jupyter NotebookStargazers:202Issues:9Issues:18

maf

Masked Autoregressive Flow

Language:PythonLicense:NOASSERTIONStargazers:195Issues:8Issues:12

Continuous-ConvOp

The Continuous Convolution Operator Tracker (C-COT).

Language:MATLABLicense:GPL-3.0Stargazers:194Issues:13Issues:14

MOTBeyondPixels

Monocular multi-object tracking using simple and complementary 3D and 2D cues (ICRA 2018)

Language:MATLABLicense:GPL-3.0Stargazers:187Issues:9Issues:9

l2hmc

TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:182Issues:21Issues:3

Neural-Decision-Forests

An implementation of the Deep Neural Decision Forests in PyTorch

Language:PythonLicense:MITStargazers:153Issues:4Issues:6

DeepTracking

Source code of DeepTracking research project

NAF

Experiments for the Neural Autoregressive Flows paper

snail

A PyTorch implementation of the blocks from the _A Simple Neural Attentive Meta-Learner_ paper

Language:PythonLicense:MITStargazers:95Issues:9Issues:3

ciwt

This repository contains code for the tracking system as described in ''Combined Image- and World-Space Tracking in Traffic Scenes'', ICRA 2017.

pyfilter

Particle filtering and sequential parameter inference in Python

Language:PythonLicense:MITStargazers:75Issues:7Issues:14

SIVI

Using neural networks to build an expressive hierarchical distribution; A variational inference method to accurately estimate the posterior uncertainty; A fast and general method for approximate Bayesian inference. (ICML 2018)

Language:MATLABLicense:MITStargazers:51Issues:5Issues:2

bssm

Bayesian Inference of State Space Models

snl

Sequential Neural Likelihood

Language:PythonLicense:MITStargazers:38Issues:4Issues:2

kernel_hmc

Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"

Language:PythonLicense:BSD-3-ClauseStargazers:24Issues:3Issues:0

masters_thesis

Real-time 3D Object Detection for Autonomous Driving

Language:PythonStargazers:6Issues:6Issues:0

SAEM-ABC

example code for the paper by Picchini & Samson 2017, "Coupling stochastic EM and Approximate Bayesian Computation for parameter inference in state-space models", arXiv:1512.04831.

Language:MATLABLicense:LGPL-3.0Stargazers:3Issues:3Issues:1

cost-optimal-particle-filter

We present demonstrative tools for exploring the themes described in Warrington and Dhir. "Generalising Cost-Optimal Particle Filtering." arXiv preprint arXiv:1805.00890 (2018).

Language:PythonLicense:MITStargazers:2Issues:0Issues:0
Language:Jupyter NotebookLicense:Apache-2.0Stargazers:1Issues:0Issues:0