po-hsuan-huang Goto Github PK
Name: Po-Hsuan Huang
Type: User
Bio: [email protected]
Location: Internet
Name: Po-Hsuan Huang
Type: User
Bio: [email protected]
Location: Internet
Documentation and Samples for the Official HN API
The most cited deep learning papers
TensorFlow - A curated list of dedicated resources http://tensorflow.org
:books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).
BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration
Modeling of biophysically plausible neural networks in various scales has provided in sights in studies ranging from basic function of neural circuitry to mechanism of memory and sleep. This approach has been shown more promising than ever as more realistic models can be implemented thanks to the rapid advance of computer technologies. Nevertheless, model complexity and size still pose significant challenges to simulation speed and reproducibility. The simulation can be accelerated either by introducing concepts of software design, or by reduce the complexity of the model. Here, we demonstrate the computational utility is optimized by employing both strategies. We transport models of different neural levels from MatLab to NEST, and compare the results of simulation and the performance of the two software. On the other hand, we reduce the complexity of single neuron model and discuss the limitation of the simplified model. Finally, the computational speed is compared. This study shows NEST enables evaluation of the relations between psychophysical data and biophysical data by realizing implementation of complicated, large-scaled biophysically plausible neural fields .
Deep Gaze II tensorflow reimplementation
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
A list of recent papers regarding deep reinforcement learning
Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.
This product is in its infancy. We are building an Airbnb for restaurant business.
A one pager for emojis on Campfire and GitHub
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Publication : https://www.researchgate.net/publication/306323997_Exploration_of_event_segmentation_theory_using_LSTM Abstract: People tend to perceive ongoing continuous activity as a series of discrete events. Event Segmentation Theory (EST) postulates humans systematically partition continuous sensorimotor information flow into events and event boundaries (Reynolds, Zacks, and Braver, 2006). Gumbsch et.al. (Gumbsch and , 2016) investigated the basis of EST in the own motor interaction capabilities, and provided a computational model that learned events and event transitions while interacting with the environment. Their architecture uses a linear forward model as event models. We proposed that Long Short Term Memory (LSTM) neural network can augment the learning capability of forward models, and learn event transitions by forming gates. Gates are the cells of LSTM whose states switch on/off when the agent detects the event boundary. The computational model can then use the gates to predict event transitions and plan actions to achieve goals. We investigated the proper parameters for gate formation in a simple scenario. These parameters include length of buffer sequence, duration of training, and size of hidden layer. We also investigated the effect of weights, and found several classes of gates. We found weights on peephole to forget gate, input neuron to output gate, and cell output to output gate the defining weights of gate formation. They selectively open and close input gates, forget gates, and output gates at event boundaries. We also found output cell the most eligible candidate of gates for event boundary prediction. This findings can serve as guidelines of designing computational models that based on LSTM .
Detect neurologically atypical behavior from eye movements
Aligning eyetracking fixation points to the video frames and extract saliency values
ffmpeg cheat sheet specific for ezvisoin
starter from "How to Train a GAN?" at NIPS2016
High Throughput, Realistic Data-Augmentation Toolkit for Opitical Digit Detection on Fabrics in Outdoors Enviroment
[Computer Vision] Semantic Segmentation Demo with Tensorflow Keras
JANNLab Neural Network Framework for Java
Attention mechanism Implementation for Keras.
My Mac dot files
Homework of Machine learning course in 2015 winter semester
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.