icemansina
I am an assistant professor at CUHKSZ. My research is about 3D computer vision and AI guided interdisciplinary research.
The University of Hong KongHong Kong
Pinned Repositories
60_Days_RL_Challenge
Learn Deep Reinforcement Learning in Depth in 60 days
ACM-BCB
Transmembrane Topology Identification by Fusing Evolutionary and Co-evolutionary Information with Cascaded Bidirectional Transformers
CUHKSZ_DIP
gluon-tutorials-zh
《动手学深度学习》
IJCAI2016
This repository is for the replication of our published paper "Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks" on IJCAI2016. Please consult and consider citing the following papers:
LayerNorm
Test LayerNorm for Lasagne
LSTM-CF
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
mxnet-the-straight-dope
An interactive book on deep learning. Much easy, so MXNet. Wow.
protein-transformer
Predicting protein structure through sequence modeling
Semantic-Segmentation-Suite
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
icemansina's Repositories
icemansina/CUHKSZ_DIP
icemansina/LSTM-CF
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
icemansina/IJCAI2016
This repository is for the replication of our published paper "Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks" on IJCAI2016. Please consult and consider citing the following papers:
icemansina/gluon-tutorials-zh
《动手学深度学习》
icemansina/mxnet-the-straight-dope
An interactive book on deep learning. Much easy, so MXNet. Wow.
icemansina/protein-transformer
Predicting protein structure through sequence modeling
icemansina/Semantic-Segmentation-Suite
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
icemansina/60_Days_RL_Challenge
Learn Deep Reinforcement Learning in Depth in 60 days
icemansina/ACM-BCB
Transmembrane Topology Identification by Fusing Evolutionary and Co-evolutionary Information with Cascaded Bidirectional Transformers
icemansina/alphafold
Open source code for AlphaFold.
icemansina/ambientocclusion
EEC277 Final Project (Winter 2012) on Ambient Occlusion with Voxels
icemansina/awesome-deep-vision
A curated list of deep learning resources for computer vision
icemansina/awesome-semantic-segmentation
awesome-semantic-segmentation
icemansina/awesome-vqa
Visual Q&A reading list
icemansina/ccnss2018_students
icemansina/claimrank
Code for the claim scoring subtask for post-modifier generation. Written at the TTIC 2018 CollaboWrite workshop.
icemansina/CliqueNet
Convolutional Neural Networks with Alternately Updated Clique (to appear in CVPR 2018)
icemansina/cs228-material
Teaching materials for the probabilistic graphical models and deep learning classes at Stanford
icemansina/deep-learning-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
icemansina/Detectron
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
icemansina/gluon-cv
Gluon CV Toolkit
icemansina/Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
icemansina/models
Models and examples built with TensorFlow
icemansina/p2rank
P2Rank: Protein-ligand binding site prediction tool based on machine learning. Stand-alone command line program / Java library for predicting ligand binding pockets from protein structure.
icemansina/pix2pixHD
Synthesizing and manipulating 2048x1024 images with conditional GANs
icemansina/PointSite_Inference
PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms
icemansina/RaptorX_Property_Fast
RaptorX-Property: a Standalone Package for Protein Structure Property Prediction
icemansina/really-awesome-gan
A list of papers on Generative Adversarial (Neural) Networks
icemansina/Tars
A simple deep generative model library in Theano and Lasagne
icemansina/tensorflow-deeplab-lfov
DeepLab-LargeFOV implemented in tensorflow