yiiwood's repositories
deep-reinforcement-learning-papers-1
A list of recent papers regarding deep reinforcement learning
ElasticFusion
Real-time dense visual SLAM system
crfasrnn
This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/
tensorflow
Open source software library for numerical computation using data flow graphs.
caffe2
This is currently an experimental refactoring of Caffe.
SuiteSparse
A shallow fork of SuiteSparse adding build files for Visual Studio and support for ACML
dvo_slam
Dense Visual Odometry and SLAM
slam-1.0-fpga
SMG-SLAM algorithm in VHDL
condnet
Implementation of condnets
Joint-Bayesian
Face verify using Joint Bayesian, implemented by Python
rgbd-slam-tutorial-gx
code for the rgbd-slam tutorial written in cnblogs
yodaqa
A Question Answering system built on top of the Apache UIMA framework.
caffe-1
some new implementation of caffe
elephas
Deep learning on Spark with Keras
lsd_slam
LSD-SLAM
sptam
S-PTAM: Stereo Parallel Tracking and Mapping
eesen
End-to-End Speech Recognition using Deep RNNs (Models), CTC (Training) and WFSTs (Decoding)
ann-writer
An artificial machine learning program that attempts to impersonate the writing style of any given text training set
fastfusion
Volumetric 3D Mapping in Real-Time on a CPU
char-rnn
Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch
BBRL
BBRL is a C++ open-source library used to compare Bayesian reinforcement learning algorithms
awesome-rnn
Recurrent Neural Network - A curated list of resources dedicated to RNN
libvisensor
Low level hardware driver for the visual inertial SLAM sensor.
OpenDTAM
An open source implementation of DTAM
optimus
Train, evaluate and deploy Convolutional Neural Network based text classifiers
or-tools
Google's Operations Research tools
deepdist
Lightning-Fast Deep Learning on Spark
WordEmbeddingAutoencoder
An autoencoder to calculate word embeddings as mentioned in Lebret/Collobert paper 2015