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Jason Lee's Projects

ijcai2016 icon 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:

lasagne icon lasagne

Lightweight library to build and train neural networks in Theano

lstm-cf icon lstm-cf

LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling

mask_rcnn icon mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

models icon models

Models and examples built with TensorFlow

neural-turing-machines icon neural-turing-machines

Attempt at implementing system described in "Neural Turing Machines." by Graves, Alex, Greg Wayne, and Ivo Danihelka. (http://arxiv.org/abs/1410.5401)

ntm-lasagne icon ntm-lasagne

Neural Turing Machines library in Theano with Lasagne

ntm-one-shot icon ntm-one-shot

One-shot Learning with Memory-Augmented Neural Networks

p2rank icon 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.

pix2pixhd icon pix2pixhd

Synthesizing and manipulating 2048x1024 images with conditional GANs

pointsite_inference icon pointsite_inference

PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms

rcnn-depth icon rcnn-depth

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

recipes icon recipes

Lasagne recipes: examples, IPython notebooks, ...

reseg icon reseg

A Recurrent Neural Network for Object Segmentation

rlntm icon rlntm

An implementation of the RL-NTM from http://arxiv.org/abs/1505.00521

rocalphago icon rocalphago

An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search" (Nature 529, 484-489, 28 Jan 2016), details of which can be found on their website https://deepmind.com/publications.html.

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